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FORECASTING BIKE TRAFFIC FOR BETTER TRAFFIC MANAGEMENT IN OTTAWA CITY
Study Group B8Gideon James Draviam - 61910680
Jasmeet Singh - 61910021
Kuheli Jati - 61910343
Mridul Mishra - 61910569
Rhishabh Garg - 61910352
Ujjwal Kejriwal - 61910587
FCAS Project
Forecasting Bike Traffic in Ottawa City to understand the requirement of increasing bike corridors at important junctions
Ottawa Traffic Department wants to estimate the bike traffic at 6 important junctions in 2019
Stakeholders and Problem Why is it needed?
• Ottawa Police documented 23% increase in road accidents• Registered vehicles went up by about 15,000, while number of
new drivers increased by approximately 5,000.
Data source: Streets of Ottawa, Ontario Canada (Kaggle)
“…look out for pedestrians, cyclists and motorcyclists” - Staff Sgt. Frank D’Aoust
Source: Globalnews.ca
Forecasting exercise will help the department in both short term and long term ways
Predict the maximum bike traffic in 2019 (Nov 2018 to Oct 2019)
One-time exercise as building corridors takes time and is cost-intensive
Model to be deployed as a current time forecast and not running forecast
Other Short Term Outcomes• Manage traffic management personnel
based on predicted forecasts
• Make-shift arrangements to increase bike
lanes in case of non-consistent forecasts
Cost Trade-off• Cost of building bike corridors (long-term) or
deploying higher manpower (short-term)
• Cost of accidents/life due to high traffic
$2M/km $9M/person
Source: C40 Cities Source: The Globalist
Understanding the data shows us the trend and weekly seasonality
5 years of data from Nov 2013 to Oct 2018
Date: The date in ISO-8601 format
COBY: NCC Eastern Canal Pathway
LMET: Laurier Segregated Bike lane
OBVW: O-Train Pathway just north of Bayview Station
OGLD: O-Train Pathway just north of Galdstone Avenue
ORPY: NCC Ottawa River Pathway
SOMO: Somerset bridge
All Counters
• Level and Increasing trend exists in the data (expected as population rises and vehicle
ownership rises)
• Data has high fluctuations which will lead to high noise
• Seasonality exists as can be observed from monthly and weekly plots
• Weekday seasonality was also observed
Trend
Seasonality
Data imputation using nearest average and aggregated at a weekly level to meet business objective
• Some series had missing data because of counter under maintenance (mainly winter season)
• Imputed with average of nearest neighbours
• Objective is to forecast the maximum bike traffic in the next year
• Data was aggregated at weekly level using the ‘maximum’ function which helped reduce noise
Extreme fluctuations in data leading to high noise
Relevant models were tried based on time series and business objective
Smoothening Regression
Seasonal NaïveMoving Average
Simple Exponential
Smoothening
Double Exponential
Smoothening
Holt’s Winters Additive
Holt’s Winter’s Multiplicative
Linear (T + Weekly
Index)
Linear (T + T^2 +
Weekly Index)
Logarithmic (T + Weekly
Index)AR Model
Level
Trend
Season
Auto-Correlation
Business Objective (No Running Forecast)
Partitioning, Benchmark, Metric of Interest & Comparison
Partitioning - Of the 5 years, 4 years data is TRAIN data and 1 year data is VALIDATION data
The Validation performance would be evaluated with 2 years also to negate isolated instances affecting the performance metric of the model
TRAIN
2 Year Validation
1 Year Validation
Benchmark - Seasonal Naive is taken as the benchmark for evaluating the models Metrics of Interest - Since the data transformation signifies the peak performance, the error of the model (SSE, MSE, RMSE & MAPE) are considered as the metrics to evaluate modelsComparison - MAPE is metric of primary interest because of scale and congruence of series
Multiplicative Holt Winter Model provides better prediction!
Forecasting the next year for LMETMethod
Multiplicative Holt-Winter
Performance
External Factors - Weather - Traffic - Holidays - Growth
We recommend corridor extension at 3 major junctions
Long Term
Short Term
Compared 99th pctl of Forecasts v/s 99th pctl of historical data to estimate the capacity of current corridors1
Historical 99th Percentile
Forecasted 99th
PercentileDifference Decision
COBY 2846 2859 0.45% No
LMET 3478 2786 -19.91% No
OBVW 1653 2055 24.32% Yes
OGLD 1648 1695 2.85% Maybe
ORPY 3946 3741 -5.20% No
SOMO 1100 1195 8.64% Yes
Corridor Cost
Life Saved2
3 x 5 km x $2M/km
1. Based on inputs from city planner in Pune2. Total 8 fatal accidents (2-wheeler) happened in 2017 . In 2010, multiple such measures were taken which brought down the rate by 50%. Assumed 40% reduction in death toll because
of corridor extension
$30M
$29M
= =
=8 lives x 40%x $9M/life
=
Since Lane Construction is a long-term project, Ottawa Traffic
Department can make temporary lanes (from roads) to better
manage traffic
THANKSThank You.