Time Series Analysis Brt Delhi

8/13/2019 Time Series Analysis Brt Delhi

1/81

TIME SERIES ANALYSIS OF VEHICULAR

DELAY IN DELHI

by

MEHVESH MUSHTAQEntry No. 2010CEP3291

Submitted

In partial fulfillment of the requirements for the award of the degree of

MASTER OF TECHNOLOGY

In

TRANSPORTATION ENGINEERING

Under the supervision of

Prof. Geetam Tiwari

Dr.A.K.Swamy

Department of Civil EngineeringIndian Institute of Technology Delhi

August, 2013


2/81

ACKNOWLEDGEMENTS

I take this opportunity to express my regards, indebtedness and profound sense of

gratitude to my supervisor Prof. Geetam Tiwari and Dr.A.K.Swamy for their

inspiring guidance, constant encouragement and ever cooperating attitude, which

enable me to undertake the present work. I appreciate their understanding, untiring

enthusiasm and the great care they took in bringing up the work in the present form.

My foremost thanks are due to my parents for their encouragement, support, love

and affection and moral boosting, which kept me going throughout the duration of

the work.

I sincerely thank Dr.Manika Agarwal(DIMTS) for providing the data used in this

project and TRIPP (Transportation Research and Injury Prevention Programme),

especially Mr.Rahul Goel, Research Scholar, TRIPP, for providing all the necessary

data and help regarding the work.

August, 2013

Mehvesh Mushtaq

(2010CEP3291)


3/81

CERTIFICATE

This is to certify that the thesis title Time Series Analysis of Vehicular Delay in

Delhi is a bonafide record of work done by Mehvesh Mushtaq for partial

fulfillment of the requirement for the degree of Master of Technology in

Transportation Engineering, Department of Civil Engineering, Indian Institute of

Technology (IIT) Delhi, New Delhi, India. She has fulfilled the requirements for the

submission of this thesis, which to the best of my knowledge has reached the

required standard.

This thesis was carried out under my supervision and guidance and has not been

submitted elsewhere for the award of any other degree.

(Dr. Geetam Tiwari)

Professor

Department of Civil Engineering,

Indian Institute of Technology Delhi,

New Delhi, India

Dr.A.K.Swamy

Assistant Professor

Department of Civil Engineering,

Indian Institute of Technology Delhi,

New Delhi, India


4/81

ABSTRACT

Speed Studies can be temporal or spatiali.e., studying speed variations over time or

over space respectively. The objective of this project is to study temporal variation

of Bus speed over various bus routes of Delhi and identify bottlenecks in traffic in

time and space. It also aims to divide a route into segments, each segment being a

part of road between Stopping points like Bus Stops, Intersections(3 ways,4 ways),

and Roundabouts. The mean speed over each segment is calculated for all hourly

time slots during which buses ply on the route for all days of the week. Thus an

hourly speed profile for all sections of the route is available for all times of busmovement.


5/81

TABLE OF CONTENTS:Chapter1.

1 Introduction

1.1 Definition of Time Series... .7

1.2 Autocorrelation... 8

1.3 Correlograms... 8

1.4 Box-Jenkins Models (Forecasting). 8

1.5 Why Time Series. 8

Chapter2.

2 Literature Review

2.1 Purpose of Literature review.10

2.2 Literature review...10

Chapter 3.

3 Data Collection and Analysis

3.1 Description of Available Data...23

3.2 Treatment of dataset123

3.3 Stationarity..24

3.4 Methodology of Project (for dataset1)..25

3.5 Results of Application of ADF test on data..26

3.6 Interpretations of Results. .27

3.7 Data Set 2..28

3.7.1 Route 108 Up.31

3.7.2 Route 108 Down...36

3.7.3 Route 185 Up....41

3.7.4 Route 185 Down.....49

3.7.5 Route 411 Up....54

3.7.6 Route 411 Down... 59

Chapter 4.

4. Conclusions .....69

4.1 Definition of Bottleneck...70

4.2 Scope for further studies...72

References....80


6/81

LIST OF FIGURES:

Figure 1: Route 108 Up.31

Figure 2: Route 108 Down.36


Figure 4: Route 185 Down49


Figure 6: Route 411 Down59

Figure 7: Comparison chart of mean speed for all routes.....69

Figure 8: Comparison of mean speeds for different time slots.71


7/81

LIST OF TABLES:

Table1. Critical values for DF and ADF tests...26

Table2. Compiled Results of ADF test on Dataset 1....27

Table3. Route Characteristics for Route 10830

Table4. Segments for Route analysis 108 Up...32

Table5.Conclusions for Route 108 Up..34

Table6. Segments for Route 108 Down36

Table7.Conclusions for Route 108 Down38

Table8. Route Characteristics for Route 18539

Table9.Segments for Route 185 Up..42

Table10.Conclusions for Route 185 Up.44

Table11. Segments for Route 185 Down..49

Table12.Conclusions for Route 185 Down...51

Table13. Segments for Route 411 Up...55

Table14.Conclusions for Route 411 Up56

Table15. Segments for Route 411 Down..60

Table16.Conclusions for Route 411 Down...62

Table17.Comparison of mean speed for different time slots.......70

Table 18. Bottleneck speed...71

Table 19. Comparison chart of Mean speeds over various Routes...73


8/81

CHAPTER - 1

INTRODUCTION

Speed Studies can be temporal or spatial i.e., studying speed variations over time

and over space respectively. The objective of this project is to study temporal

variation of Bus speed over various bus routes of Delhi and identify bottlenecks in

traffic in time and space. It also aims to divide a route into segments, each segment

being a part of road between Stopping points like Bus Stops, Intersections (Three

ways,Four ways), and Roundabouts. The mean speed over each segment is

calculated for all hourly time slots during which buses ply on the route for all days of

the week. Thus an hourly speed profile for all sections of the route is available for all

times of bus movement.

The data used for this study is GPS (Global Positioning System) Data which

provides the location of a Particular bus after regular intervals of time (in the data

used for this project it is 10 secs approx.).This is ideal for studying the data as a time

series as a Time-series is essentially an ordered sequence of values of a variable atequally spaced time intervals.

Introduction to Time-Series

1.1. Definition of Time Series: An ordered sequence of values of a variable at

equally spaced time intervals. Time series analysis accounts for the fact that data

points taken over time may have an internal structure (such as autocorrelation, trend

or seasonal variation) that should be accounted for.

1.1.1. Applications:The usage of time series models is twofold:

a) Obtain an understanding of the underlying forces and structure that produced

the observed data.

b) Fit a model and proceed to forecasting, monitoring or even feedback and feed

forward control.


9/81

1.1.2. Types of time series:

I) Continuous vs. Discrete:

Continuousobservations made continuously in time;

Discreteobservations made only at certain times.

II) Stationary vs. Non-stationary:

StationaryData that fluctuate around a constant value;

Non-stationary A series having parameters of the cycle (i.e., length,

amplitude or phase) change over time.

III) Deterministic vs. Stochastic:

Deterministic time seriesThis data can be predicted exactly;

Stochastic time series Data are only partly determined by past values and future

values have to be described with a probability distribution. This is the case for most,

if not all, natural time series. So many factors involved in a natural system that we

cannot possibly correctly apply all of them.

1.2 Autocorrelation: A series of data may have observations that are not

independent of one another. To find out if autocorrelation exist, Autocorrelation

Coefficients measure correlations between observations a certain distance apart.

1.3 Correlograms: The autocorrelation coefficient r(k) can then be plotted against

the lag (k) to develop a correlogram. This will give us a visual look at a range of

correlation coefficients at relevant time lags so that significant values may be seen.

1.4 Box-Jenkins Models (Forecasting): Box and Jenkins developed theAutoRegressive Integrative Moving Average (ARIMA) model which combined the

AutoRegressive (AR) and Moving Average (MA) models developed earlier with a

differencing factor that removes in trend in the data.

1.5 Why Time Series?

What we need from a modeling technique or a data-analysis tool is an ability to

respond quickly, provide simple forecasting techniques and ability to provide

accurate detailed local forecasts.


10/81

Limitations of traditional Complex Model Systems and Model Packages:

a) Data collection and preparation is an enormous task (because of behavioraland socio-economic variables).

b) Results are less accurate than trend extrapolation or experts judgmentc) Forecasting errors:

As much as 90% for a 7-year forecast

Average error for 7 yr forecast30%

Average error for 3 yr forecast20% (Horowitz and Enslie, 1978)

d) Techniques for short-range planning are simpler but still inaccurate.

Also, The response of the most popular of these techniques (decomposition,

exponential smoothing, moving average),to significant traffic changes is inadequate,

hence they cannot predict traffic volume or other such variables with accuracy

(Holmesland (1979)).

Time Series analysis:

Time series has recently become a more attractive tool for traffic engineers.

Traditionally, traffic engineers do not explicitly assume that successive events are

correlated and usually consider events in the time domain to vary randomly around a

trend line. Autocorrelation was also ignored because the calculation and adjustment

required for it, was tedious. However, recently developed computer software makes

this a much easier and very inexpensive process.

In summary, time-series analysis is an attractive tool for analysis because:

a) we have exhausted most of the possibilities within the old set of forecastingtechniques,

b) many of these existing techniques are not giving us good solutions,c) we have the tools to extend our work into consideration of autocorrelated

events.


11/81

CHAPTER - 2

LITERATURE REVIEW

2.1. Purpose of Literature Review:

The aim of the literature review is to summarize the major work done in the study of

travel time variation. It includes study of travel time variation, modeling of travel

time, travel time prediction.

2.2. Literature ReviewPaper no.1

Title: Analysis of travel time variation over multiple sections of Hanshin

Expressway in Japan

The paper classifies sources of uncertainty in travel time into the categories:

demand-side factors ( like traffic volume), supply-side factors (like traffic accidents)

and external effects (like rainfall intensity).It also classifies seven sources of events

that cause travel time variation: Traffic-influence events( traffic incidents, work

zones, weather),traffic demand events(fluctuations in normal traffic, special

events),physical highway features(traffic control devices and bottlenecks).The

Seemingly Unrelated Regression Equations (SURE) model was used by the authors,

as opposed to traditional models like Multiple Linear Regression (MLR) model as

the latter fail to estimate the error correlation across various equations, also called

contemporaneous error correlation. The papers novelty is also in that it considers

the effect of uncertainties on travel-time variation across multiple sections.

Methodology: The study assumes a linear relationship between the travel-time and

the factors affecting the travel-time variation. The route is divided into 3 sections

based on on-ramp and off-ramp criteria. The sections are considered dependent and

hence, the error covariance across the equation is not zero. Since it is believed that

there could be several unobserved characteristics of the uncertainties among various

sections that will affect the travel-time variation, therefore the error terms can be


12/81

correlated across sections. Therefore, the regression equations are estimated jointly

as a set of Seemingly Unrelated Regression Equations.

Travel-time estimation for the study area: Using the collected vehicle detector data,

spot speed for every 500mt interval was estimated. Corresponding travel-times were

estimated from these. Path travel time for the three sections were estimated using

time-slice method, which was found to be more suitable for offline application rather

than online application when speed varies over time. Time-slice method was also

found to provide better results than the instantaneous method.

Travel time statistical parameters like mean, median, standard deviation, probability,

cumulative distribution and standard deviation were calculated.MLR analysis was

carried out to understand the influence of all the incidents on travel-time variation.

The residual error obtained by this model was used for estimating the error

covariance matrix. Using the error covariance matrix, SURE model coefficients were

estimated.

Conclusions:

The Standard Error (SE) obtained using the SURE model for the three sections was

lower than the MLR model. The model coefficients obtained by this method werefound to be more appropriate than those obtained from the MLR model. The

coefficients estimated by the MLR model underestimate the travel time as compared

to the SURE model. Except for free-flow situations, the results obtained by the

independent models have over-estimated the travel time under the influence of

correlation among various sections due to traffic-volume (demand-side factor),

traffic-accident (supply-side factor) and rainfall (external factor).

Paper no: 2

Title: Bus Arrival Time Prediction Using Artificial Neural Network Model.

Aim: The aim of this work was to develop and apply a model to predict bus arrival

time using AVL (Automatic Vehicle Location) data. The data considered are traffic

congestion and dwell time data.

Methodology: A historical data based model, regression models and an artificial

neural network model were used. The difference between the predicted and observedarrival times was used to qualify accuracy.


13/81

AVL data was collected in Houston, Texas over 6 months in 2000(from June to

November) by Houston Metro buses equipped with DGPS (Differential Global

Positioning System) receiver at 5 second interval. The test bed was Route 60 which

was highly congested in the morning and afternoon peaks; it had two corridors, a

downtown and a north area corridor, and only the south-bound direction was studied.

The input variables were arrival time, dwell time, and schedule adherence. The time

periods were weekday peak, weekday nonpeak, weekday evening, weekend. It was

found that the variability of dwell time is larger than that of arrival time.

Models:

Historical Data Based Model: Link travel time between transit stops is calculated. It

includes stopped delay at intersections but does not include dwell times. Arrival

times are calculated at transit stops.

Regression Models: Five multiple linear regression specifications were tested in this

research after analyzing stepwise regression and correlation coefficient. Dwell time

was not used to develop regression models since it was not important statistically.

Artificial Neural Network Models: ANNs emulate the learning process of the

human brain. They are calibrated in two steps: training, and testing. Out of 13different training functions, Levernberg-Marquardt optimization algorithm was

chosen as the training function.

The ANN architecture used had three layers: an input layer, a hidden layer, an output

layer. The weights and parameters associated with the hidden layer were identified

during the calibration process. Fifteen different hidden neurons were tested and the

best number of neurons was selected for each ANN model based on the concept of

minimizing the prediction error. The prediction results from the fifteen different

neurons were not significantly different from each other. The back propagation

algorithm and the Hyperbolic Tangent Sigmoid transfer function were used in the

model development.

After testing fourteen different learning functions, a Perceptron Weight and Bias

learning function was used. The average MAPE (mean absolute percentage error) for

these fourteen functions was not significantly different.


14/81

Model Evaluation: The MAPE was used as a MOE (measure of effectiveness) in this

work. It was found that clustering data led to smaller MAPE in Historic data based

model and regression models. However, the clustering results in poorer results than

the non-clustering option in the artificial neural network models. It is, therefore,

hypothesized that ANN as a universal function approximator, was able to identify

the non-linear relationships associated with different clusters. However, there may

not have been enough observations to adequately fit the functions.

The lowest MAPE of the Historical model of downtown area was for the weekday

peak. It is proposed that congestion reduces the variability in travel times and this

makes the historical model more accurate for this time period. The use of Real-time

schedule adherence did not improve the results much and hence, it was proposed that

there is a non-linear relationship between arrival time and schedule adherence. The

ANN has the lowest MAPE as compared to the Historic model and the MLR

(Multiple Linear Regression) model. It was proposed that the use of historic data

(representing congestion) coupled with real-time schedule adherence data

(representing real-time congestion and demand inputs) resulted in better

performance of the ANN model.

Conclusions: This paper describes the results of three bus travel time prediction

algorithms which were calibrated and tested on a transit route in Houston, Texas.

The input to the models consisted of historic data (i.e., link travel time and dwell

time) and real-time schedule adherence data. It was found that the Artificial Neural

Network models (used without clustering of the data) performed considerably better

than either a historic data based model or MLR models. It was hypothesized that

ANN was able to identify the complex non-linear relationship between travel-time

and the independent variables and this led to the superior results.

Paper no.3

Title: Using bus Travel Time Data to Estimate Travel Times on Urban Corridors.

Aim: This study determines whether transit vehicles/buses can be used as probe

vehicles for collecting travel time data for automobiles on urban corridors. It

analyses the nature of information collected by the buses and develops formulas to

covert the travel time of a bus to that of an automobile. Data on bus and automobile


15/81

travel time on various sections of arterials in the northern part of New Castle

County, Delaware was used for this purpose.

Methodology:

The tasks involved are: a) to measure the travel time of the bus and the automobile

for the same section, b) to analyze the characteristics of the components of the Bus

Travel Time (BTT) and there variability, c) to develop a model that converts the

travel time of the bus to the average travel time of the automobile, d) to verify the

model by the collected data. The procedure is to convert the BTT to the ATT

(Automobile Travel Time) that is expected before the next BTT data are updated.

The predicted travel time is assumed to be equal to the estimate obtained from the

last available BTT. It is assumed that the predicted travel time would be closer to the

actual travel time if the data is collected at shorter intervals, which depends on the

frequency of buses (or measurement intervals).

The required accuracy of the predictions was debated, since a higher accuracy

complicates the measurement plans and the procedure of conversion (BTT to

ATT).On the other hand, a much lower accuracy of prediction may render the

predictions useless. Assigning a monetary value to the travel time and value assigned

to differences between predicted and actual travel time, the tolerable error of

estimate was found to be 10% to 15% of the actual travel time. The distance over

which travel time was estimated worked out to 4.6km (assuming a 55km/hr speed

and a travel time of 5min).

The difference of BTT from the average travel time of the stream is a random

variable. Buses typically, travel in the rightmost lane of the urban corridors and this

induces a bias in the travel time of buses. However, despite the sources of

randomness and bias in the difference between ATT and BTT, buses run on heavily

travelled urban corridors (at a high frequency during peak hours), follow traffic rules

and observe speed limits. These characteristics make them attractive as probe

vehicles.

Postulating that the differences between ATT and BTT arise because of the

following factors: the stopping time of the bus at bus stops, the time lost by the bus

because of repeated accelerations and decelerations from and to a stop, basicdifference between the operating abilities of the bus and the automobile, adherence


16/81

(by the bus and the automobile) to the posted speed limits, the tendency of the bus to

use the right lane; a simple predictive equation treating the actual running time of the

bus as an independent variable is formed. The equation was changed repeatedly

taking into account various factors: statistical importance of calibration constants,

insight provided by the calibration constants into the relation between the variables,

effort to make the model as calibration free as possible.

Results:

Five models were developed for the five sites and the results are presented in

equations (i) and (ii):

= + 0.14 (i)

For less frequently congested roads

= + (0.18) (ii)

For more frequently congested roads

Using this result, we can predict the average travel time of the automobile from the

data on the BTT and the general characteristics of the road section. Although five

sites may not be enough to develop a rule of thumb, such a rule may be developed

after the study of many more arterial sections. The use of AVL equipped buses as a

data source is promising because the measurement function is already available by

default and the task of prediction can be performed with minimum manual

intervention.

Paper No.4 :

Title: Chaotic analysis of traffic time series.

Authors: Pengjian Shang, Xuewei Li, Santi Kamae

Input Variables: Speed, volume, occupancy collected every 20s.

Aim: Paper applies non-linear time series modelling techniques to analyse the traffic

data collected from Beijing Xizhimen

Methodology: Raw data screened for errors, aggregated into 2min data, average

speed, average volume, total occupancy calculated. Draw curves for correlation


17/81

function v/s r, range of scaling region, from this plot the chaotic nature of traffic

time series is known, the slope of the line in the scaling region is the correlation

dimension. Correlation dimension v/s Embedding dimension is plotted. Phase space

is reconstructed using "method of delays. The slope values corresponding to the

largest Lyapunov exponent were obtained after the least-squares line fit for the

average speed time series and was found to be 0.25 (deviation +- 0.02).

Results: Saturation of correlation dimension beyond a certain embedding dimension

value is an indication of the presence of deterministic dynamics, the finite and low

correlation dimension is an of the existence of deterministic dynamics. Positive

value of Lyapunov Exponent is a strong indicator of chaos.

Conclusions:Traffic time series is deterministic and can be modelled using phase

space techniques. the predicting length of the traffic time series should be about

8min.

Paper No.5

Title: Use of the Box and Jenkins Time Series Technique in Traffic forecasting

Authors: Nancy L. Nihan and K Jello O. Holmesland, Department of Civil

Engineering, University of Washington, Seattle, U.S.A.

Input Variables: Average Weekday Volume (AWD) (1968-1977)

Aim: To show the short-range accuracy of the simplest possible model, to

investigate the accuracy of the Box-Jenkins technique for short-range forecasting

(12-month forecasting period).

Methodology: 2 steps were followed: 1) data fitting,2) model selection. After

examining several models and conducting many statistical tests, ARIMA

(Autoregressive Integrated Moving Average) was finally choosen. Two types of

forecasts - a simple forecast and an adaptive forecast were made. All errors were

found to be around 5% or less.

Results: It was found that it is possible to fit an ARIMA model as well as a

multiplicative model to traffic data from the highway under consideration, using the

Box and Jenkins technique. The ARIMA model selected was only two percent awayfrom the measured values at the end of a twelve-month forecast, ARIMA is,


18/81

therefore, highly accurate and easy to use after it has been estimated. It requires less

data input, and is flexible. It can accommodate more than one interval in complex

time series models, It can be used to relate two or more time series, and changes

taking place in a time series can be detected very soon, so it can be used as an early

warning system.

Paper No. 6.

Title: A multivariate state space approach for urban traffic flow modeling and

prediction.

Authors: Anthony Stathopoulos, Matthew G. Karlaftis, Department of

Transportation Planning and Engineering, School of Civil Engineering, National

Technical University of Athens.

Input Variables: 3-min volume measurements from urban arterial streets near

downtown Athens, flow (volume) and occupancy data used to estimate speed and

travel time.

Aim: Developing flexible and explicitly multivariate time-series state space models

using core urban area loop detector data, to model and predict flow at an urban

signalized arterial.

Methodology: Data from 144 loop locations, 5 sequential (multivariate setting)

detectors along an important 3-lane per direction signalized arterial on the periphery

of the core area of the city (Alexandras Avenue) are chosen for further analysis.

Time series is tested for stationarity using the augmented Dickey Fuller (ADF) test.

Determination of basic autoregressive and cross-correlation characteristics of the

time series for the various loop locations, State space models were developed for

both the pooled data (data from all time periods combined), and the data from the

various periods separately. The models developed were flexible for an

autoregressive and a moving average order of up to three lags. 70% of the data was

used for model development and 30% for testing.

Results: Predictions obtained from the state space models are superior to those

obtained from the ARIMA models; in one of the loops, the state space model yields


19/81

a mean absolute percent error (MAPE) of 12% compared to a 20% MAPE value

from the ARIMA model; MAPE values reported here are rather high when compared

to values reported in other studies.

Conclusions: Despite its potential usefulness, traffic flow in signalized urban

arterials cannot be predicted, at least in the short-run, with as much accuracy as flow

in urban freeways. The results of the models developed clearly suggest that, at least

in the case of Athens, different specifications are appropriate for different time

periods. Further, it also appears that the use of multivariate state space models is

relevant in the urban roadway system.

Paper No.7.

Title: A time-series analysis of public transit ridership in Portland,Oregon,1971-

1982.

Authors: Michael Kyte, James Stoner, Jonathan Cryer.

Input Variables: level of transit service available, (2) relative costs of travel by

transit and by automobile, (3) the size of the travel market, and (4) other factors such

as gasoline shortages, weather, etc.

Aim: Comprehensive analysis of public transit usage in Portland, Oregon, from 1971

through 1982 using time-series analysis, The impacts of the 81 service changes and 5

fare changes implemented have been analyzed at both at the system and route levels

using transfer function and intervention time-series models, the effects of auto travel

costs and the local economy are included.

Methodology: A) Model development: Transfer function model is chosen and

Impact Analysis and Intervention analysis are conducted, B) forecast procedure was

accomplished by not using the final one year of data (July 1981-June 1982) for the

system data and fall 1981-spring 1982 for the route-level data), estimating the

models, and then making forecasts for 12 months or four quarters ahead. The

forecasts were then compared with the actual ridership data. C) Three levels of data

aggregation were used: system level, sector level, and route level. Three classes of

time-series models were developed including univariate transfer function,

intervention and simultaneous equations transfer function.


20/81

Results: The effects of service-level and fare changes on transit ridership are not

instantaneous but are delayed and distributed over specific periods of time. The

existence of these lag structures is expected from consumer behavior theory. The

effect of fare changes can be measured for up to three months after their

implementation. Gasoline price and employment level changes do have immediate

effects with no discernable lag structures. Feedback relationships were identified

between transit ridership, fare, service level, and gasoline price. For example,

gasoline price changes affect future transit fare changes. The service-level and fare

elasticities computed for the system, the six sectors, and the 26 routes were in the

range reported by previous studies. Models were generally consistent, in terms of lag

structure and elasticities, between the three data aggregation levels. However, some

variables are inherently more effective at one level than another. The system models

had mean absolute percent errors (MAPE) of less than five percent.

Paper No.8.

Title: Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA

Process: Theoretical Basis and Empirical Results.

Authors: Billy M. Williams and Lester A. Hoel.

Aim: To present a case for acceptance of a specific time series formulationthe

seasonal autoregressive moving average processas the appropriate parametric

model for a specific type of ITS (Intelligent Transportation System) forecast: short-

term traffic condition forecasts at a fixed location in the network, based only on

previous observations at the forecast location.

Methodology: A) theoretical justification for the application of ARIMA models:

assertion that a weekly seasonal difference will yield a stationary transformation of

discrete time traffic condition data series, coupled with the Wold decomposition

theorem, B) Hypothesis: properly fitted seasonal ARIMA models will provide

accurate traffic condition forecasts, C) Testing of hypothesis through empirical

results, where correlation analysis is shown as the basis for assessing the stationarity

of series transformations using a first weekly difference; presentation of the model-

fitting results and a discussion of the heuristic benchmarks used to assess the

predictive performance of the fitted seasonal ARIMA models.


21/81

Results: One-step seasonal ARIMA predictions consistently outperformed heuristic

forecast benchmarks. Assertions and findings presented in this paper directly

contradict a statement in Kirby et al. 1997, namely that extending simple ARIMA

models to include seasonal and other effects, in practice... did not have a

substantial impact on the results. Theoretical foundation for seasonal ARIMA

modeling negates any theoretical motivation to investigate high level nonlinear

mapping approaches, such as neural networks. This assertion is supported by

comparison to actual neural network forecasting results with a common data set.

Paper No.9

Title: Multivariate Short-Term Traffic Flow Forecasting Using Time-Series

Analysis.

Authors: Bidisha Ghosh, Biswajit Basu and Margaret OMahony.

Input Variables: traffic flow, number of maneuvers, time.

Aim: Introducing a different class of time-series models called structural time-series

model (STM) (in its multivariate form) to develop a parsimonious and

computationally simple multivariate short-term traffic condition forecasting

algorithm.

Methodology: A) A "seemingly unrelated time-series equation (SUTSE) Model,

which is also a multi-inputmulti-output short-term traffic flow forecasting model,

where the number of input intersections is more than number of output intersections

is choosen. B) The proposed multivariate SUTSE traffic flow forecasting

methodology is applied to a congested urban transportation network at the city center

of Dublin. A network of ten intersections within the transport network is chosen for

this purpose. C)The univariate traffic flow observations obtained over each 15-min

interval from the inductive loop detectors situated at these ten intersections and their

nearest available upstream junctions are modeled using the proposed multivariate

traffic flow model. D) The cross-correlational structure of the ten chosen junctions is

verified. E) Traffic flow time series is reduced to stationary form by 'differencing,

second-order stationarity of the time-series data sets used in the MST model are

checked by plotting the autocorrelation functions (ACFs) of the data sets, To ensure

stationarity, seasonal differencing has been performed on all the modeled traffic flow


22/81

time-series data sets. All of the ten series of traffic flow observations are modeled

using homogeneous SUTSE models.

Results: Hyperparameter estimates and the plot of the seasonal component show

that the seasonality is deterministic in nature. The trend component is stochastic and

depicts the within-day local fluctuations in the data. The trend component varies

about a zero mean value, validating the assumption that there is no slope component

latent within the traffic flow data set. The SUTSE model is computationally more

efficient than some of the other existing multivariate short-term traffic flow

forecasting methodologies.The checking for stationarity conditions is not critical to

the development of the model. The developed SUTSE model can separately trace the

evolution of each individual component (trend, seasonality, etc.) of the traffic flow

data over time. Consequently, the deterministic nature of the seasonal component of

the traffic volume observations from junctions at urban signalized arterials has been

established.MST model can additionally include the effect of changes in traffic

conditions at one or more immediate upstream junctions to improve the predictions

at the downstream output junction.

Paper No.10.

Title: Travel Time Prediction using a Seasonal Autoregressive Integrated Moving

Average Time Series Model.

Authors: Angshuman Guin.

Input Variables: Volume, average speed and lane-occupancy data.

Aim: Investigating the possibility of extending the Box and Jenkins technique to

develop a Seasonal ARIMA (also sometimes referred to as SARIMA) prediction

model for travel times.

Methodology: Average speed converted into travel time, several weeks travel time

data is plotted over time, plots are superimposed and the periodicity is detected, The

ACF( Autocorrelation Function) plot of the raw travel time data for all weekdays is

plotted, the ACF plot of the single lag (15-minute interval) differenced travel time

data is plotted, the ACF plots for a dataset with just the Mondays of the consecutive

weeks is plotted, ACF of Raw Monday Travel Time Data (10 days) is plotted tocheck for stationarity, the first difference of the series is plotted but it does not yield


23/81

any stationarity, the autocorrelation plots indicate that a seasonal differencing at

weekly level would generate a stationary series. A multiplicative seasonal

autoregressive integrated moving average process of period s, with regular and

seasonal AR (Autoregressive) orders p and P, regular and seasonal MA orders q and

Q, and regular and seasonal differences ,is referred to as an ARIMA (p,d,q)(P,D,Q)s

model.

Conclusions: Travel times have strong weekly seasonality, such seasonality is to be

expected at higher levels of aggregation and not in the system level data at which the

detectors record the data, weekly periodicity can be successfully used in a predictive

model for segment travel times, this model is expected to provide effective travel

time forecasts irrespective of whether the travel time estimates are based on point

detection data, probe vehicle data or Automatic Vehicle Identification (AVI) data.

Relevance of Literature review:

Paper no.3 , Using bus Travel Time Data to Estimate Travel Times on Urban

Corridors which is included in the literature review is particularly useful because it

considers the use of data collected from Buses fitted with AVL (Automated Vehicle

Location) equipment for estimating travel time for motor vehicles on the same

routes. The data being used in this project has been collected in a similar manner

from buses fitted with GPS equipment. Hence, the models/methodology used in the

papers can be used to see how the data available with us can be used to estimate

travel times for all vehicles on the routes covered.


24/81

CHAPTER - 3

DATA COLLECTION AND ANALYSIS

3.1 Description of Available Data:

Data Set 1:

The data has been collected over a period of 1 week for Route 419(BRT corridor)

The data has been collected using GPS enabled AVL ( Automated Vehicle

Location) project implemented by DIMTS (Delhi Integrated Multi-Modal TransitSystem Ltd) that record certain parameters at every 10 seconds for every bus.

The parameters recorded are: latitude, longitude, timestamp, speed, distance

travelled by object, user_id( registration of the bus).

The available data covers the 24 hour schedule of the bus. The bus route generally

varies from 9.30 am in the morning to 7pm in the evenings. Since the buses return do

circular routes, the data for the return journey is not recorded by the GPS.

Dataset 2:

The GPS data over various bus routes of Delhi collected from 1-1-2013 to 31-1-

2013.

The parameters recorded are: latitude, longitude, timestamp, speed, distance

travelled by object, user_id( registration of the bus).

The data is already separated into UP and DOWN trips for all routes.

3.2 Treatment of Dataset 1:

The data available was present in the form of Latitude and Longitude and time

stamp, hence the first step was the conversion of these to distance and speed values.

Using the spherical law of cosines formula (equation (iii)) distance is calculated

from latitude, longitude and R (radius of earth);
http://mathworld.wolfram.com/SphericalTrigonometry.htmlhttp://mathworld.wolfram.com/SphericalTrigonometry.html


25/81

Spherical law of cosines:

= (acossin1 .sin2+ cos1 . cos2 . cos (iii)

3.3 Stationarity:

A stationary time series is one whose statistical properties such as mean, variance,

autocorrelation, etc. are all constant over time. Most statistical forecasting methods

are based on the assumption that the time series can be rendered approximately

stationary (i.e., "stationarized") through the use of mathematical transformations. A

stationarized series is relatively easy to predict: you simply predict that its statistical

properties will be the same in the future as they have been in the past.

Another reason for trying to stationarize a time series is to be able to obtain

meaningful sample statistics such as means, variances, and correlations with other

variables. Such statistics are useful as descriptors of future behavior onlyif the series

is stationary. For example, if the series is consistently increasing over time, the

sample mean and variance will grow with the size of the sample, and they will

always underestimate the mean and variance in future periods. And if the mean and

variance of a series are not well-defined, then neither are its correlations with other

variables. For this reason it is important to be cautious about trying to

extrapolate regression models fitted to non-stationary data.

First Difference:

The first difference of a time series is the series of changes from one period to the

next. If Y(t) denotes the value of the time series Y at period t, then the first

difference of Y at period t is equal to Y(t)-Y(t-1). In Statgraphics, the first difference

of Y is expressed as DIFF(Y). If the first difference of Y is stationary and

also completely random(not autocorrelated), then Y is described by a random

walk model: each value is a random step away from the previous value. If the first

difference of Y is stationary but not completely random--i.e., if its value at period t is

autocorrelated with its value at earlier periods--then a more sophisticated forecasting

model such as exponential smoothing or ARIMA may be appropriate. (Note: ifDIFF(Y) is stationary and random, this indicates that a random walk model is
http://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htmhttp://people.duke.edu/~rnau/411rand.htm


26/81

appropriate for the original series Y, not that a random walk model should be fitted

to DIFF(Y). Fitting a random walk model to Y is logically equivalent to fitting a

mean (constant-only) model to DIFF(Y)).

Test for Stationarity:

If the series has a stable long-run trend and tends to revert to the trend line following

a disturbance, it may be possible to stationarize it by de-trending (e.g., by fitting a

trend line and subtracting it out prior to fitting a model, or else by including the time

index as an independent variable in a regression or ARIMA model), perhaps in

conjunction with logging or deflating. Such a series is said to be trend-

stationary. However, sometimes even de-trending is not sufficient to make the

series stationary, in which case it may be necessary to transform it into a series of

period-to-period and/or season-to-season differences. If the mean, variance, and

autocorrelations of the original series are not constant in time, even after detrending,

perhaps the statistics of the changes in the series between periods or between

seasons will be constant. Such a series is said to be difference-

stationary. (Sometimes it can be hard to tell the difference between a series that is

trend-stationary and one that is difference-stationary, and a so-called unit root

testmay be used to get a more definitive answer.

3.4 Methodology of Project (for DataSet-1)

1) Testing for Stationarity :-KPSS Test (KwiatkowskiPhillipsSchmidtShin Test)

-ADF Test (Augmented DickeyFuller test)

2) Trend AnalysisDefinition of Non-Stationarity:

A Unit root test tests whether atime series variable is non-stationary using

anautoregressive model. A well-known test that is valid in large samples is

theaugmented DickeyFuller test.The optimal finite sample tests for a unit root in

autoregressive models were developed by John Denis Sargan andAlok Bhargava.

These tests use the existence of aunit root as thenull hypothesis.
http://en.wikipedia.org/wiki/Time_serieshttp://en.wikipedia.org/wiki/Autoregressivehttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/John_Denis_Sarganhttp://en.wikipedia.org/wiki/Alok_Bhargavahttp://en.wikipedia.org/wiki/Unit_roothttp://en.wikipedia.org/wiki/Null_hypothesishttp://en.wikipedia.org/wiki/Null_hypothesishttp://en.wikipedia.org/wiki/Unit_roothttp://en.wikipedia.org/wiki/Alok_Bhargavahttp://en.wikipedia.org/wiki/John_Denis_Sarganhttp://en.wikipedia.org/wiki/Augmented_Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Autoregressivehttp://en.wikipedia.org/wiki/Time_series


27/81

ADF Test:

An Augmented DickeyFuller test (ADF) is an augmented version of theDickey

Fuller test for a larger and more complicated set of time series models. The

augmented DickeyFuller (ADF) statistic, used in the test, is a negative number. The

more negative it is, the stronger the rejection of the hypothesis that there is a unit

roots at some level of confidence.

3.5 Results of Application of ADF Test on Data:

The ADF test was applied on the data for each run (the bus performs 4 runs in a

day), and the results obtained were compiled in Table 2 and were compared to the

critical values for DF (Dickey-Fuller) and ADF (Augmented Dickey-Fuller) tests

provided in Table 1.The ADF values were greater than the critical values for the test.

Table 1: Critical values for DF and ADF Tests (Fuller, 1976, p373)

Significance level 10% 5% 1%

C.V. for constant but no trend -2.57 -2.86 -3.43

C.V. for constant and trend -3.12 -3.41 -3.96
http://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_testhttp://en.wikipedia.org/wiki/Dickey%E2%80%93Fuller_test


28/81

Table 2. Compiled Results of ADF test on Dataset 1:

S.No

.

Type

ADF test

statistic

1%

-3.96

5%

-3.41

10%

-3.12

Durbin

Watson

Statistic

1 1strun,1stdifference,trend+in

tercept,lag 10 -8.625 -3.982 -3.421 -3.133 1.996

2 2n -run,1stdifference,

trend+intercept., lag 10 -9.545 -3.983 -3.422 -3.134 2.003

3 3r

-run,1st-difference,

trend+intercept, lag 10 -10.202 -3.987 -3.424 -3.135 1.998

4 4th run,1st difference, lag

10 -11.630 -3.984 -3.422 -3.134 1.997

5 1st run,1st difference, trend

only, lag 10 -9.558 -3.448 -2.869 -2.570 2.003

6 1strun,2n -difference,

trend+intercept, lag 10 -10.559 -3.982 -3.421 -3.133 2.016

(Test results in CD-Appendix 1)

3.6 Interpretation of results:

Since the ADF value is always greater than the critical values, hence the null

hypothesis is rejected. The series is stationary.


29/81

Interpreting the DurbinWatson statistic:

Since d (DurbinWatson statistic) is approximately equal to 2(1 r), where r is the

sample autocorrelation of the residuals, d = 2 indicates no autocorrelation. The value

of d always lies between 0 and 4. If the DurbinWatson statistic is substantially less

than 2, there is evidence of positive serial correlation. As a rough rule of thumb, if

DurbinWatson is less than 1.0, there may be cause for alarm. Small values

of d indicate successive error terms are, on average, close in value to one another, or

positively correlated. If d > 2 successive error terms are, on average, much different

in value to one another, i.e., negatively correlated. In regressions, this can imply an

underestimation of the level ofstatistical significance.

The DurbinWatson statistic approaches 2 in most of the tests, suggesting that

autocorrelation does not exist.

3.7 Dataset-2

Description of data:

The data provided by DIMTS was 1 month (1/1/13 to 31/1/13) data for different bus

routes of Delhi.

Methodology:

The bus routes were divided into segments, using Bus stops, Intersections (3

way/4way) and Roundabouts as Segment ends. The mean speed for each segment

was calculated and these were tabulated to see trends over time. The speeds are

tabulated as Results according to Day of week and according to Hourly Time Slot

(Appendix A of CD). A graph of each table is added at the end. The Bottlenecks

exposed via these graphs (segments having low speeds) are tabulated in

Conclusionstables. The range of speed for each Time slot and Day for each route

is also provided in the Conclusions Table.

Procedure:

The data was provided by DIMTS as Combined Data file per route. This was divided

into separate files (using MATLAB software) for separate days and for separate

buses. Speed calculations were carried out on these to determine the instantaneous
http://en.wikipedia.org/wiki/Statistical_significancehttp://en.wikipedia.org/wiki/Statistical_significance


30/81

speed along the routes. The routes were divided into segments according to

occurrence of bus-stop/traffic light/intersection and the mean speed was calculated

for each segment. The results for a particular time slot for many buses for a

particular segment were averaged to give an estimate of mean speed at a particular

segment of a route at a particular time (1 hour time slot).These were aggregated

according to day of week. By this, a speed profile of every day of the week was

obtained for all the routes studied.

Following are the Route Data, Segment ends and Conclusions for each route:


31/81

Route: 108 Up/Down:

The route lies between the ends Hari Nagar Clock Tower and Nehru Vihar.Table

3 summarizes the Route Characteristics.

Table 3: Route Characteristics for Route 108

Route No. 108

Depot:- Low Floor

RUNNING TIME:- 64 Minutes

Departure Time

Hari Nagar. Clock Tower Nehru Vihar

0536 1048 1624 0648 1200 1736

0552 1104 1640 0704 1216 1752

0608 1120 1656 0720 1232 1808

0616 1128 1704 0728 1240 1816

0624 1136 1712 0736 1248 1824

0640 1152 1728 0752 1304 1840

0656 1208 1744 0808 1320 1920

0712 1224 1800 0824 1336 1936

0800 1400 1848 0904 1512 2024

0816 1416 1928 0928 1528 2040

0832 1432 1944 0944 1544 2056

0840 1440 1952 0952 1552 2104

0848 1448 2000 1000 1600 2112

0904 1504 2016 1016 1616 2128

0920 1520 2032 1056 1632 2144

0936 1536 2048 1112 1648 2200


32/81

Fig 1 : Route 108 UP

3.7.1 108 UP (Nehru Vihar Road to Hari nagar Crossing)

The route is divided into segments, the segments being separated by Bus Stopping

points like Bus stops, Intersections (3 way/4 way, Roundabout)( Table 4).The

Bottlenecks identified for different time slots and days are summarized in Table 5.


33/81

Table 4: Segments of Route for analysis:

1 Nehru vihar Road 28.71015 77.22426

2 Fourway 28.70975 77.22698

3 Nehru vihar crossing 28.70885 77.22599

4 Police station timarpur 28.70686 77.22412

5 Balak ram hospital 28.70505 77.22322

6 Timar pur 28.70061 77.22125

7 Timarpur water tank 28.69746 77.2206

8 North mall road 28.69428 77.21986

9 Mall road 28.69342 77.21887

10 International students hostel 28.69608 77.21178

11 Fourway 28.69652 77.2108

12 Khalsa college 28.69511 77.20993

13 Patel chest(4 way) 28.69175 77.20827

14 Sri ram college 28.68906 77.20697

15 Daulat ram college 28.68805 77.20646

16 Maurice nagar 28.68676 77.20586

17 Roop nagar 28.68462 77.20369

18 Roop nagar 28.68413 77.20250

19 Kamla nagar 28.68320 77.20108

20 Nangia park(roundabout) 28.67991 77.19585

21 Intersection 28.67772 77.18900

22 Leela vati vidya mandir 28.67712 77.18904

23 Gulabi bagh crossing 28.67516 77.18870

24 Shastri nagar 28.67261 77.18622

25 Gulabi bagh 28.67097 77.18412

26 Fourway 28.67007 77.18349

27 DDA flats sarai basti 28.67097 77.17615

28 Shiv mandir 28.67160 77.17313

29 Inderlok 28.67206 77.16894

30 Intersection 28.67079 77.16655

31 Zakhira road 28.66876 77.16523


34/81

32 Zakhira 28.66759 77.16421

33 Roundabout 28.66680 77.16387

34 DCM chemicals 28.66521 77.15986

35 Campa cola 28.66199 77.1521136 ESI dispensary 28.66081 77.14885

37 Intersection 28.65961 77.14603

38 Moti nagar market 28.65733 77.14178

39 Threeway 28.65456 77.13680

40 Roundabout 28.65286 77.13842

41 F block 28.65150 77.14076

42 Kirti nagar ps 28.64952 77.1438243 Furniture market 28.64764 77.14236

44 Saraswati garden 28.64600 77.14028

45 Wood market 28.64279 77.13713

46 Man sarovar garden 28.63815 77.13241

47 Fourway 28.63754 77.13065

48 Mayapuri depot 28.63670 77.12868

49 Govt. press mayapuri 28.63432 77.1277750 Maya puri metal forging 28.63087 77.12479

51 LIG flats 28.63077 77.11937

52 Swarag ashram 28.63088 77.11519

53 Beri-wala bagh 28.63103 77.11247

54 Round-about 28.63160 77.11163

55 DDU hospital 28.62824 77.11103

56 Hari nagar clock tower 28.62469 77.11039


35/81

Table 5. Conclusions about Route 108Up

Slot/Day: Bottleneck: Mean speed:

Sundays 1) Nehru Vihar road to Four way (0.93kmph) 9.02kmph to

35.50kmph

Mondays 1) Four way after Mayawati Garden to Mayapuri

depot (4.91kmph)

6.95kmph to

35.30kmph

Tuesdays 1) Mayapuri depot to Govt. press mayapuri

(6.9kmph)

9.20kmph to

30.80kmph

Wednesdays 1) Four way after International students hostel to

Khalsa College (5.37kmph)

2) Harinagar clock tower(1.37kmph)

8.50kmph to

35.46kmph

Thursdays 1) Leelawati vidya mandir to Gulabi bagh crossing

(4.44kmph)

2) Four way after man sarovar garden to Mayapuri

depot (5.75kmph)

8.67kmph to

32.50kmph

Fridays 1) Kamlanagar to Nangia park(roundabout)

(4.25kmph)2) Harinagar clock tower(4.5kmph)

8.42kmph to

35.01kmph

Saturdays 1) Four way after Mansarovar garden to Mayapuri

depot(5.96kmph)


9.26kmph to

34.90kmph

8am-9am 1) Four way after Mansarovar garden to Mayapuri

depot(5.14kmph)


10.83kmph to

37.20kmph


depot(6.06kmph)

9.10kmph to

34.90kmph


depot(4.46kmph)

9.08kmph to

33.87kmph

11am-

12noon

1) Four way after Mansarovar garden to Mayapuri

depot(5.68kmph)

2) Harinagar clock tower (5.41kmph)

9.30 kmph to

34.37kmph

12noon-1pm 9.79kmph to


36/81

32.46kmph

1pm-2pm 1) Khalsa College to Patel chock (5.37kmph)

2) Nangia Park to Intersection(5.57kmph)

8.76kmph to

32.69kmph

2pm-3pm 1) Four way after Mansarovar garden to Mayapuridepot(4.91kmph)

8.54kmph to30.56kmph

3pm-4pm 1) Kamlanagar to Nangia park(5.91kmph) 8.43kmph to

32.55kmph

4pm-5pm 1) Nehru vihar road to Four way(1.11kmph)

2) Kamlanagar to Nangia park( roundabout)


depot(4.91kmph)

8.06kmph to

34.15kmph


2) Kamlanagar to Nangia park(5.10kmph)


depot(5.96kmph)

7.28kmph to

33.45kmph



7.58kmph to

31.36kmph

7pm-8pm 1) Nehru Vihar to Four way(0.93kmph)


3) Leelawati vidya mandir to Gilabi bagh

crossing(4.44kmph)


depot(5.75kmph)

7.82kmph to

34.38kmph


37/81

3.7.2 Route: 108 Down

The route lies between the ends DDU Hospital and Balak Ram Hospital(Fig2).The

route is divided into segments, the segments being separated by Bus Stopping points

like Bus stops, Intersections (3 way/4 way, Roundabout)( Table 6).The Bottlenecks

identified for different time slots and days are summarized in Table 7.

Fig2: Route 108 Down

Table 6. Segments for Route 108 Down:

S.No.

Segment

from/to -> Latitude Longitude S.No Segment from/to -> Latitude Longitu

1 DDU hospital 28.62805 77.11088 27 Inderlok 28.67142 77.167

2 Roundabout 28.63170 77.11136 28 Fourway 28.67253 77.1694

3 Beri wala bagh 28.63147 77.11202 29 Shiv mandir 28.67186 77.173

4 Swarg asharam 28.63097 77.11485 30 Shastri nagar E block 28.67126 77.176

5 Swarg asharam 28.63101 77.11510 31 Fourway 28.67003 77.1832

6 LIG flats 28.63091 77.11946 32 Gulabi bagh 28.67106 77.183

7

(Four - way)

Ram singh 28.62992 77.12359 33

Shastri nagar A

block 28.67284 77.186


38/81

marg

8

Maya puri

metal forging 28.63064 77.12427 34 Gulabi bagh crossing 28.67489 77.188

9 Govt press 28.63512 77.12792 35Swami narayanmarg(Three way) 28.67552 77.188

10 Fourway 28.63743 77.12977 36 Leelawati mandir 28.67706 77.188

11

Man sarovar

garden 28.63843 77.13249 37 Roundabout 28.67927 77.1938

12 Chuna bhati 28.63911 77.13343 38 Fourway 28.68142 77.1980

13 Wood market 28.6426 77.13673 39 Kamla nagar 28.68239 77.1994

14

Saraswati

garden 28.64608 77.14004 40

Roop nagar(Bus Stop

befpre roundabout) 28.68409 77.202

15

Furniture

market 28.6478 77.14225 41

Roop nagar(Bue Stop

after roundabout) 28.6847 77.203

16 Kirti nagar 28.6497 77.14374 42 Maurice nagar 28.68629 77.205

17

F block kirti

nagar 28.65138 77.14079 43 Sri ram college 28.68893 77.206

18 Rounabout 28.65245 77.13908 44 Patel chest 28.69144 77.207

19 Kirti nagar 28.65483 77.13681 45 Patel chest 28.69228 77.208

20

Moti nagar

market 28.65721 77.14111 46 Khalsa college 28.69446 77.209

21 Moti nagar 28.65882 77.14419 47 Threeway 28.6965 77.2105

22 ESI dispensary 28.66047 77.14731 48

International students

hostel 28.69625 77.211

23 Campa cola 28.66273 77.15335 49 Entrance to side road 28.69532 77.214

24 DCM chemicals 28.66521 77.15943 50

Lucknow road govt

school 28.69577 77.216

25 Roundabout 28.66649 77.16335 51 Lucknow road 28.69686 77.2166

26 Zakhira 28.66766 77.16407 52 Balak ram hospital 28.70521 77.223


39/81

Table 7. Conclusions for Route 108 Down:


Sundays 1) Govt. press to Fourway

2) Roundabout after Leelawati mandir to following

Four - way(


40/81

(4.52kmph)

2) Four - way to Kamlanagar (5.9kmph)

34.09kmph

2pm-3pm 1) Four - way to Man sarovar garden (6kmph)

2) Four - way to Kamlanagar (5.61kmph)

9.44kmph to

30.80kmph

3pm-4pm 1)Roundabout to Four - way (4.24kmph)

2) Four - way to Kamlanagar(6.26kmph)

9.92kmph to

31.28kmph

4pm-5pm 1) Roundabout to Four - way(6.67kmph)

2) Four - way to Kamlanagar(5.65kmph)

11.10kmph to

32.25kmph

5pm-6pm 1) Four - way to Mansarovar garden (6.8kmph)

2) Roundabout to Four - way (4.25kmph)

9.89kmph to

31.80kmph

6pm-7pm 1) Four - way to Kamlanagar(6.14kmph)

2) Kamlanagar to Roopnagar(6.16kmph)

8.80kmph to

31.50kmph

7pm-8pm 1) Four - way to Mansarovar garden (6.27kmph)

2) Motinagar to ESI dispensary (4.9kmph)

3) Roundabout after Leelawati mandir to Four - way

(5.2kmph)

7.11kmph to

35.60kmph

Route: 185

The route lies between the ends Nathupura and Kendriya Terminal. Route characteristics are

summarized in Table 8.

Table 8: Route Characteristics for Route 185

Route No. 185

Depot:- Standard Floor

Running Time:-90 Mintues Nathu Pura to Kendriya Tr., Nathu Pura

to I.S.B.T. 60 Minutes

Departure Time

Nathu Pura I.S.B.T. Kend. Terminal

0500 1511 0605 1524 0924

0605 1524 0710 1603 0950

0631 1550 0736 1616 1021

0710 1611 0815 1655 1125


41/81

0749 1620 0905 1747 1704

0800 1629 0933 1813 1746

0815 1642 1020 1905 1804

0828 1655 1051 2010 18300841 1708 1115 2023 1856

0946 1721 1209 2045

0950 1800 1230 2049

1010 1905 1255 2110

1104 1918 1335 2141

1121 1938 1419 2205

1151 1944 1445 22201230 1950 1458 2233

1314 2005 1511 2305

1340 2036

1353 2100

1406 2115

1419 2128

1458 2200


42/81

3.7.3 Route: 185 UP

The route is from Kendriya Terminal to Nathupura(Fig 3). The route is divided into

segments, the segments being separated by Bus Stopping points like Bus stops,

Intersections (3 way/4 way, Roundabout)(Table 9).The Bottlenecks identified for

different time slots and days are summarized in Table 10.

Figure 3 : Route 185 Up


43/81

Table 9: Segments for 185 Up

S.No.

Segment from/to-

> Latitude Longitude

Segment from/to-

> Latitude Longitude

1

Kendriya

terminal 28.61744 77.20421 35

Civil lines metro

station 28.67641 77.22499

2 Roundabout 28.61739 77.20555 36 IP College 28.68005 77.22356

3

Kendriya

terminal 28.61951 77.20612 37

Three -

way(Mahatma

Gandhi road) 28.68127 77.22274

4

Kendriya

terminal 28.62167 77.20623 38 Old Secretariat 28.68407 77.222095 NDPO 28.62559 77.20649 39 Khyber Pass 28.69017 77.22118

6 Roundabout 28.62653 77.20747 40

Three - way(north

mall road) 28.69318 77.21982

7

Gurudwara

bangle sahib 28.62546 77.20938 41 Mall Road 28.69348 77.21887

8 YMCA 28.62623 77.21243 42

International

Students Hostel 28.69612 77.21182

9 Jantar Mantar 28.62790 77.21596 43 GTB nagar 28.69828 77.20605

10 Palika Kendra 28.62872 77.21652 44

Three -

way(Mahatma

gandhi marg) 28.69874 77.20483

11 Regal Cinema 28.63073 77.21742 45 Camp Chock 28.69928 77.20482

12

Three -

way(Panchkurian

road joins inner

circle) 28.63423 77.21697 46 T.B.Hospital 28.70052 77.20508

13 Shivaji Park 28.63857 77.22388 47 Gandhi Ashram 28.70466 77.20510

14

New Delhi

Railway Station 28.64145 77.22606 48 Daka Village 28.70710 77.20461

15 Roundabout 28.64241 77.22680 49

Permanand

Crossing 28.70870 77.20452

16 JL Nehru Marg 28.64207 77.22812 50 Radio Colony 28.71123 77.20436


44/81

17

Zakir Hussain

College 28.64116 77.23001 51 Nirankar Colony 28.71458 77.20402

18

Three -

way(jawahar lal

nehru marg) 28.64074 77.23079 52

CB Raman IIT

Colony 28.72103 77.19988

19 Asaf Ali College 28.64202 77.23281 53

Nirankari Sarovar

Burari Crossing 28.72703 77.19780

20

Delhi Nagar

Nigam 28.64122 77.23520 54 Nirankari Sarovar 28.72736 77.19783

21 Hotel Broadway 28.64086 77.23838 55

Four - way(outer

ring road) 28.72808 77.19787

22 Darya Ganj 28.64319 77.24036 56

Transport

Authority 28.73074 77.19837

23 Subhash Park 28.64899 77.23957 57 Jharoda Diary 28.73491 77.19745

24 Three - way 28.64971 77.23905 58 St. Nagar 28.73828 77.19744

25 Jama Masjid 28.65124 77.23790 59 Bengali Colony 28.73871 77.19756

26 Red Fort 28.65365 77.23639 60 Francis School 28.74477 77.19802

27

Four - way(

shyam prasad

mukherjee marg) 28.65958 77.23631 61

Sarvodaya

Vidyalay Burari 28.74897 77.19841

28 GPO 28.66180 77.23479 62 Burari Village 28.75331 77.19881

29 GGS University 28.66534 77.23012 63 Burai Ghari 28.75755 77.19560

30 ISBT 28.66840 77.22757 64 Laxmi vihar 28.76002 77.19093

31

Three - way(lala

hardev sahai

marg) 28.66845 77.22660 65 Kaushik Enclave 28.76080 77.18918

32

Three - way

(ISBT) to Shyam

nath marg 28.66881 77.22659 66 Amrit Vihar 28.76413 77.18379

33 Ludlow Castle 28.67211 77.22593 67 Nathupura 28.76894 77.18060

34

Three -

way(Shyam nath

marg) 28.67304 77.22568


45/81

Table 10. Conclusions about Route 185 UP:


Sundays 1) Transport Authority to Four - way(5.36kmph)2) Intersection after DTC ambedkar terminal to Delhi

gate(6.08kmph)

3) Kendriya Terminal(8.44kmph)

10.08kmph to

36.87kmph

Mondays 1) Nathupora to Amrit vihar colony(1.01kmph)2) Kaushik enclave to Laxmi vihar(5.09kmph)3) Transport Authority to Four way(4.10kmph)4) Intersection(Jawaharlal Nehru marg) to Delhi

Gate(5.94kmph)

5) Three - way(outer circle,Barakhamba road) to Three -way(outer circle,Kasturba Gandhi marg)(5.59kmph)

6) Kendriya terminal Bus Stop to Kendriya terminal(5.01kmph)

8.66kmph to

36.09kmph

Tuesdays 1) Nathupora to Amrit Vihar Colony(3.10kmph)2) Four - way after Transport Authority to Burari

Crossing(4.64kmph)3) ISBT to Yamuna Bazaar(4.02kmph)4) Intersection(Jawahar lal Nehru marg) to Delhi

Gate(3.56kmph)

5) Delhi Gate to LNJP hospital(5.37kmph)6) Three - way(outer circle,Barakhamba road) to Three -

way(outer circle,Kasturba bagndhi marg)(3.04kmph)

7) St.Columbus School to Kendriya Terminal(3.72kmph)

8.82kmph to

39.84kmph

Wednesdays 1) Nathupora to Amrit Vihar Colony(5.59kmph)2) Transport Authority to Four way(3.07kmph)3) Intersection(Jawahar lal nehru marg) to Delhi

Gate(4.96kmph)

4) Kendriya Terminal (6.0kmph)

9.21kmph to

41.20kmph

Thursdays 1) Transport Authority to Four - way(3.77kmph)2) Intersection(Jawahar lal Nehru marg) to Delhi

7.23kmph to

37.76kmph


46/81

gate(4.67kmph)

3) Three - way(outer circle,barakhamba road) to Three -way(outer circle,Kasturba Gandhi marg)(6.05kmph)

4) Kendriya terminal(0kmph)Fridays 1) Nathupora to Amrit Vihar Colony(6.26kmph)

2) Transport Authority to Four - way(4.09kmph)3) Intersection(Jawahar lal Nehru marg to Delhi

gate(5.14kmph)

4) Four - way( maharaja ranjit singh marg) to Statesmanhouse(6.41kmph)

5) Three - way(outer circle,barakhamba road) to Three -way(outer circle,kasturba Gandhi marg)(7.64kmph)

6) Three - way( near Janpath) to Three - way(outercircle,Sansad Marg)(6.24kmph)


8.82kmph to

39.06kmph

Saturdays 1) Nathupora to Amrit Vihar Colony(4.87kmph)2) Transport Authority to Four - way(4.05kmph)3) Intersection(Jawahar lal Nehru marg) to Delhi

Gate(6.83kmph)

4) Statesman house to Three - way(outercircle,Barakhamba road)(3.91kmph)

5) Kendriya terminal(3.49kmph)

8.87kmph to

36.94kmph

8am-9am 1) New Delhi Railway station to Roundabout(7kmph)2) Subhash Park to Three - way(7.63kmph)3) Four way(outer ring road) to Transport

Authority(0.44kmph)

4) Sarvodaya Vidyalaya Burari to BurariVillage(5.6kmph)

5.00kmph to

39.30kmph

9am-10am 1) New Delhi Railway Station to Roundabout(6.98kmph)2) Three - way after Subhash Park to Jama

Masjid(6.35kmph)

3) GPO to GCS university(6.72kmph)4) Four - way(outer ring road) to Transport

7.79kmph to

34.15kmph


47/81

Authority(4.6kmph)

5) Transport Authority to Jharoda Diary(3.41kmph)10am-11am 1) Kendriya Terminal to Roundabout(1.53kmph)

2) Three - way after Subhash park to JamaMasjid(7.12kmph)

1.53kmph to

37.55kmph

11am-

12noon

1) Kendriya Terminal to Roundabout(1.16kmph)2) Subhash Park to Three - way(2.26kmph)3) International Students Hostel to GTB nagar(7.55kmph)4) Permanand Crossing to Radio Colony(5.57kmph)5) Four -way(outer ring road) to Transport

Authority(3.48kmph)

2.29kmph to

35.38kmph

12noon-1pm 1) New Delhi Railway Station to Roundabout(5.19kmph)2) Subhash Park to Three - way(2.53kmph)3) Four - way(shyam Prasad Mukherjee marg) to

GPO(5.25kmph)

4) Permanand Crossing to Radio Colony(2.62kmph)5) Four - way(outer ring road) to Transport

Authority(3.28kmph)

6) Nathupura(0kmph)

4.14kmph to

38.32kmph

1pm-2pm 1) Darya Ganj to Subhash Park(2.16kmph)2) Jama Masjid to Red Fort(5.34kmph)3) Camp Chock to TB Hospital(4.81kmph)4) Permanand Crossing to Radio Colony(3.70kmph)5) Four - way(outer ring road) to Transport

Authority(3.37kmph)

6) Nathupora(0kmph)

3.63kmph to

34.78kmph

2pm-3pm 1) Subhash Park to Three - way(3.02kmph)2) Jama Masjid to Red Fort(4.32kmph)3) Red Fort to FourFour way( Ram Prasad Mukjarjee

Marg)(4.35kmph)

4) Four - way(Shyam Prasad Mukherjee Marg) toGPO(4.22kmph)

5) GTB nagar to Three - way(Mahatma Gandhi Marg)(4

3.20kmph to

36.09kmph


48/81

.41kmph)

6) Camp Chock to T.B Hospital(3.49kmph)7) T.B Hospital to Gandhi Ashram(5.17kmph)8) Four - way(outer ring road) to Transport

Authority(3.81kmph)

9) Nathupora(0kmph)3pm-4pm 1) New Delhi Railway Station to Four - way(6.93kmph)

2) Hotel Broadway to Darya Ganj(5.48kmph)3) Subhash park to Three - way (4.09kmph)4) Four - way(Shyam Prasad Mukherjee marg) to GPO

(6.72kmph)

5) Four - way(outer ring road) to TransportAuthority(3.32kmph)

6) Nathupora(0kmph)

5.29kmph to

33.47kmph

4pm-5pm 1) Hotel Broadway to Darya Ganj(4.35kmph)2) Darya Ganj to Subhash park(4.68kmph)3) Subhash park to Three - way(4.90kmph)4) Red fort to 4way(shyam Prasad mukherjee

marg)(4.67kmph)

5) Four - way(Shyam Prasad mukherjee marg)toGPO(3.72kmph)

6) Permanand Crossing to Radio crossing(6.88kmph)7) Four - way(outer ring road) to Transport

Authority(3.27kmph)

8) Nathupora(0kmph)

4.70kmph to

36.09kmph

5pm-6pm 1) Regal cinema to Three - way(4.71kmph)2) New Delhi railway station to roundabout(7.50kmph)3) Hotel Broadway to Daryaganj(6.97kmph)4) Darya ganj to Subhash park(5.93kmph)5) Subhash park to Three - way(6.18kmph)6) Red fort to Four - way(4.07kmph)7) Four - way to GPO(3.13kmph)8) Four - way(outer ring road) to Transport

4.37kmph to

33.20kmph


49/81

Authority(4.43kmph)

6pm-7pm 1) Three - way(Jawahar lal Nehru marg) to Asif alicollege(2.93kmph)

2) Subhash park to Three - way(3.83kmph)3) Four - way(shyam Prasad mukherjee marg) to

GPO(3.85kmph)

4) Four way(outer ring road) to TransportAuthority(4.43kmph)

5) Nathupora(0kmph)

5.26kmph to

34.07kmph

7pm-8pm 1) International Students Hostel to GTB nagar(7.40kmph)2) Four - way(outer ring road) to Transport

Authority(4.47kmph)

3) Bengali Colony to Francis School(5.82kmph)

6.01 kmph to

32.12kmph


50/81

3.7.4 185 Down:

The route is from Nathupora to Kendriya Terminal (Fig 4). The route is divided into

segments, the segments being separated by Bus Stopping points like Bus stops,

Intersections (Three way/Four way, Roundabout) (Table 11).The Bottlenecks

identified for different time slots and days are summarized in Table 12.

Fig4: 185 Down

Table 11. Segments for Route 185 Down

S.No. Segment Latitude Longitude Segment Latitude Longitud

1 Nathupura 28.76885 77.18087 27 Merging traffic 28.66067 77.2452

2

Amrit vihar

colony 28.76477 77.18364 28 Intersection 28.65108 77.2457

3 Kaushik enclave 28.76090 77.18938 29 Shanti van 28.64967 77.2456

4 Laxmi vihar 28.76000 77.19114 30 Raj ghat 28.64174 77.2469

5 Burari ganj 28.75791 77.19536 31 Intersection 28.64003 77.2469

6 Burari village 28.75522 77.19871 32

DTC ambedkar

terminal 28.63965 77.2438


51/81

7 Burari xing 28.75441 77.19908 33

Intersection(jawahar

lal nehru marg) 28.64008 77.24113

8

Sarvodaya

vidtalay burari 28.74862 77.19853 34 Delhi gate 28.63998 77.2387

9 Francis school 28.74470 77.19812 35 LNJP hospital 28.64019 77.235

10 Bengali colony 28.73870 77.19756 36

Intersection/Jawaharlal

nehru marg 28.64037 77.23215

11 Jharoda diary 28.73477 77.19763 37

Four way(maharaja

ranjit singh marg) 28.62907 77.2266

12

Transport

authority 28.73319 77.19803 38 Statesman house 28.63030 77.2239

13 Four way 28.72879 77.19812 39

Three way( outer

circle ,barakhamba

road) 28.63102 77.22269

14 Burari crossing 28.72821 77.20028 40

Three way(outer

circle,Kasturba gandhi

marg) 28.63028 77.22139

15

Three way-

intersection 28.72235 77.21614 41

Three way(near

janpath) 28.6299 77.2197

16 Gandhi vihar 28.72150 77.21874 42

Three way(Outer

circle,Sansad marg) 28.63025 77.2181

17

Gopal pur

crossing 28.71920 77.22386 43 Hanuman mandir 28.63003 77.2136

18 Three way 28.70923 77.22732 44

Gurudwara bangla

sahib 28.62830 77.2097

19 PWD office 28.70264 77.22773 45 St.columbus school 28.62739 77.2076

20 Majni ka tilla 28.69767 77.22732 46 Kendriya terminal 28.62358 77.2064

21 Three way 28.69682 77.22729 47 Kendriya terminal 28.62172 77.2063

22 Matkaf house 28.68344 77.22920 48 Roundabout 28.62091 77.20639


24 ISBT 28.67118 77.23141 50 Roundabout 28.61772 77.20604

25 Yamuna bazaar 28.66481 77.23538 51 Kendriya terminal bs 28.61734 77.2044



52/81

Table 12. Conclusions about 185 Down:


Sundays 1) Transport Authority to Four way( 5.36kmph) 10.08kmph to

36.87kmph

Mondays 1) Nathupora to Amrit vihar colony(1.01kmph)

2) Kaushik enclave to Laxmi vihar(5.09kmph)

3) Transport Authority to Four way(4.10kmph)

4) Intersection(Jawaharlal Nehru marg) to Delhi

Gate(4.90kmph)

8.6kmph to 36.00

kmph

Tuesdays 1) Nathupura(3.10kmph)2) Four way after transport authority to Burari

crossing(4.60kmph)

3) ISBT to Yamuna bazaar(4.02kmph)4) Intersection(Jawahar lal Nehru marg) to Delhi

gate(3.50kmph)

5) Three way(outer circle,barakhamba road) to Threeway(Kasturba Gandhi marg)(3.04kmph)

6) St. Columbus school to Kendriyaterminal(3.70kmph)

8.82 kmph to

39.80kmph

Wednesdays 1) Nathupora to Amrit Vihar colony(5.5kmph)2) Transport authority to Four way(4.87kmph)3) Intersection( Jawaharlal Nehru marg)to Delhi

Gate(4.96kmph)

9.20kmph to

41.20kmph

Thursdays 1) Transport authority to 4 way(3.77kmph)2) Intersection( Jawahar lal Nehru marg) to Delhi gate(

4.67kmph)

3) Kendriya terminal (0 kmph)

7.23kmph to

37.76kmph

Fridays 1) Transport Aurhority to Four way(4.09kmph)2) Intersection( Jawahar lal Nehru marg) to Delhi

gate(5.14kmph)

3)

Kendriya Terminal(4.43kmph)

8.82kmph to

39.06kmph


53/81

Saturdays 1) Nathupura to Amrit Vihar colony(4.80kmph)2) Transport Authority to Four way(4.05kmph)3) Statesman house to Three way(outer

circle,barakhamba road)(3.90kmph)


8.8kmph to 36.90kmph

8am-9am Nathupura to Amrit Vihar colony(3.10kmph) 5.48kmph to

27.22kmph

9am-10am Intersection after DTC ambedkar terminal to Delhi Gate

(5.94kmph)

10.72kmph to

34.28kmph

10am-11am 1) Transport Authority to Three way(4.02kmph)2) Intersection after DTC Ambedkar nagar Terminal to

LNJP hospital

9.03kmph to

37.30kmph

11am-

12noon

1) Transport Authority to Four way(4.05kmph)2) Intersection after DTC ambedkar terminal to Delhi

Gate(4.90kmph)

7.25 kmph to

35.50kmph

12noon-

1pm

1) Kaushik enclave to Laxmi vihar(5.09kmph)2) Transport Authority to Four way(3.07kmph)3) ISBT to Yamuna Bazaar(3.65kmph)4) Intersection after DTC Ambedkar nagar Terminal to

Delhi Gate(4.9kmph)

5) Three way(outer circle,barakhamba road) to Threeway(outer circle,Kasturba Gandhi marg)(5.33kmph)

6) Kedriya Terminal (6.00kmph)

8.87kmph to

36.90kmph

1pm-2pm 1) Transport Authority to Four way( 5.47kmph)2) Intersection after DTC Ambedkar Terminal to Delhi

Gate(5.47kmph)

9.26kmph to

37.07kmph

2pm-3pm 1) Transport Authority to Four way(6.24kmph)2) Intersection after DTC Ambedkar terminal to Delhi

Gate(5.22kmph)

3) Three way(outer circle,barakhamba road) to Threeway(outer circle,kasturba Gandhi marg)(2.33kmph)

6.16kmph to

35.50kmph

3pm-4pm 1) Nathupura to Amrit Vihar colony(1.01kmph)2) ISBT to Yamuna bazaar(3.38kmph) 8.44kmph to


54/81

3) Delhi Gate to LNJP hospital(5.37kmph)4) Three way(outer circle,barakhamba road) to Three

way(outer circle,kasturba Gandhi marg)

5) St.Columbus school to KendriyaTerminal(3.72kmph)

40.58kmph

4pm-5pm 1) Nathupura to Amrit Vihar colony(4.8kmph)2) ISBT to Yamuna Bazaar(4.02kmph)3) Intersection after DTC Ambedkar Terminal to Delhi

Gate(4.50kmph)

4) Three way( outer circle,barakhamba road to Threeway,outer circle,kasturba Gandhi marg)

5) Kendriya Terminal bs to KendriyaTerminal(5.01kmph)

9.3kmph to 41.20kmph

5pm-6pm 1) Statesman house to Three way(outercircle,barakhamba road)(4.60kmph)

2) Kendriya Terminal(5.12kmph)9.35kmph to

34.09kmph

6pm-7pm 1) Intersection after DTC Ambedkar terminal to DelhiGate(5.14kmph)

2) Kendriya terminal(3.40kmph)8.77kmph to

32.60kmph

7pm-8pm 1) Intersection after DTC Ambedkar terminal to DelhiGate(3.56kmph)

2) Statesman House to Three way(outercircle,Barakhamba road)(3.91kmph)

10.76kmph to

39.06kmph


55/81

3.7.5 Route: 411 Up

The route is from Nityanand Marg to Ambedkar Terminal(Fig 5). The route is

divided into segments, the segments being separated by Bus Stopping points like

Bus stops, Intersections (Three way/Four way, Roundabout)(Table 13).The


Fig 5: Route 411 up


56/81

Table 13. List of Bus-stops/Traffic Lights used for segmentation:

Bus Stop/Traffic

Light Latitude Longitude BS/TL Latitude Longitude

1 Nityanand marg 28.66843 77.22476 35 Sri niwaspura 28.56672 77.25294

2 ISBT 28.66873 77.22657 36

Lajpat nagar

crossing 28.56451 77.25026

3 Kashmiri gate 28.66945 77.22824 37 Garhi village 28.56231 77.2515

4

Maharana pratab

isbt 28.66921 77.22883 38

B block east

kailash 28.56205 77.25565

5 GCS university 28.66546 77.23025 39

C block east

kailash 28.56204 77.25834

6 GPO 28.66190 77.23503 40 SNP depot 28.56122 77.26014

7 Four way 28.66031 77.23616 41 Modi mill 28.55672 77.26438

8 Red fort 28.65815 77.23694 42 Three way 28.55543 77.26549

9 Jama masjid 28.65036 77.23875 43

Modi mill

crossing 28.55615 77.26754

10 Subhash park 28.64902 77.23956 44 Modi mill 28.55534 77.26675

11 Darya ganj 28.64570 77.24040 45 Modi mill 28.55435 77.26541

12 Delhi gate 28.64093 77.24095 46 NSIC 28.55220 77.26446

13

Ambedkar

stadium terminal 28.63977 77.24367 47 Three way 28.54931 77.26257

14 Three way 28.63960 77.24649 48 Kalkaji mandir 28.54811 77.26289

15 IG stadium 28.63296 77.24712 49

Govind puri

metro station 28.54520 77.26415

16 IP power station 28.62458 77.24734 50 Kalkaji depot 28.53827 77.26688

17 IP depot 28.61973 77.24891 51 C lal chock 28.53425 77.26851

18

Pragati power

station 28.61590 77.25008 52 Intersection 28.52929 77.27123

19 Three way 28.61325 77.25096 53

Hilgiri

apartments 28.52859 77.27041

20 Pragati maidan 28.61141 77.24606 54

Tughlaqabad

extension 28.52550 77.26079


57/81

21 Three way 28.61279 77.24032 55 Three way 28.52635 77.25747

22 National stadium 28.61058 77.24017 56 Tara apartments 28.52425 77.25623

23 Zoo 28.60858 77.24012 57 Tughlaqabad 28.52386 77.25610

24 Intersection 28.60735 77.24017 58Guru ravidasashram 28.51984 77.25456

25 Intersection 28.60585 77.24029 59

Guru ravidas

mandir 28.51947 77.25439

26 Sundernagar 28.60216 77.24056 60 Hamdard nagar 28.51721 77.25357

27

Delhi public

school 28.59872 77.24081 61

Apollo-

pharmacy 28.51566 77.25302

28 Roundabout 28.59361 77.24347 62 Three way 28.51201 77.25195

29

Dargah hazrat

nizammudins 28.59139 77.24501 63 Hamdard nagar 28.51193 77.24962

30

Hazrat

nizammudins 28.5896 77.24628 64

Satyanarayan

mandir 28.51222 77.24305

31 Bhogal 28.58296 77.25135 65 Vayusena bad 28.51278 77.23899

32 Ashram 28.57449 77.25683 66 Tigri 28.51291 77.23841

33 Four way 28.57290 77.25808 67 Devli crossing 28.51376 77.23485

34 Nehru nagar 28.56864 77.25413 68

Ambedkarnagar

terminal 28.51507 77.22894

Table 14. Conclusions for 411 Up.


9am-10am


58/81

university bus station

3) Four way to Redfort4) Subhash park to Darya ganj5) Three way to National Stadium6) Ambedkarnagar terminal

11am-

12noon


59/81

6) Ambedkarnagar terminal5pm-6pm


60/81

3.7.6 Route: 411 down

The route is from Ambedkarnagar terminal to Nityanand Marg(Fig 6). The route is

divided into segments, the segments being separated by Bus Stopping points like

Bus stops, Intersections (Three way/Four way, Roundabout)(Table 15).The


Fig 6: Route 411 Down


61/81

Table 15. List of Bus-stops/Traffic Lights used for segmentation:

Bus

stop/Traffic

Light

Latitude Longitude Bus Stop/Traffic

Light

latitude Longitude

1 Ambedkar

nagar terminal

28.51568 77.22654 35 Nehru nagar 28.56897 77.25382

2 RPS Colony 28.51522 77.22873 36 Four way 28.57220 77.25770

3 Devli crossing 28.51410 77.23375 37 Ashram 28.57371 77.25729

4 Vayusena bad 28.51394 77.23458 38 Bhogal 28.58191 77.25166

5 Tigri 28.51291 77.23890 39 Jangpura 28.58477 77.24957

6 Sri

satyanarayan

mandir

28.51234 77.24317 40 Hazrat nizammudin 28.58932 77.24631

7 Sangam vihar 28.51206 77.24850 41 Dargah hazrat

nizammundin

28.59169 77.24462

8 Hamdard nagar 28.51205 77.24939 42 Roundabout 28.59273 77.24379

9 Three way 28.51193 77.25141 43

Documents

Time Series Analysis Brt Delhi