EXPLOITING NEW SENSOR TECHNOLOGIES FOR …docs.trb.org/prp/14-1673.pdfEXPLOITING NEW SENSOR TECHNOLOGIES FOR REAL-TIME PARKING ... street occupancy estimation in ... takes advantage

Vlahogianni, Kepaptsoglou, Tsetsos, Karlaftis 1

EXPLOITING NEW SENSOR TECHNOLOGIES FOR REAL-TIME PARKING PREDICTION IN URBAN AREAS

Eleni I. Vlahogianni*, Ph.D. Department of Transportation Planning & Engineering

School of Civil Engineering National Technical University of Athens

5 Iroon Polytechniou Str, Zografou Campus, 15773 Athens Greece Tel: +30 210 772 1369; Fax: +30 210 772 1454

e-mail: [email protected]

Kostanstinos Kepaptsoglou, Ph.D. School of Rural and Surveying Engineering

National Technical University of Athens, 5 Iroon Polytechniou Str, Zografou Campus, 157 73 Athens Greece

Tel: +30 210 772 3167; Fax: +30 210 772 2404 e-mail: [email protected]

Vassileios Tsetsos, Ph.D.

Mobics Ltd 27 Kifissias Avenue, 11523 Athens Greece


Matthew G. Karlaftis, Ph.D.

Department of Transportation Planning & Engineering School of Civil Engineering

National Technical University of Athens 5 Iroon Polytechniou Str, Zografou Campus, 15773 Athens Greece


Word Count: 6241

Tables: 2 Figures: 9

*corresponding author

TRB 2014 Annual Meeting Original paper submittal - not revised by author


ABSTRACT This paper proposes a methodological framework - based on survival analysis and neural networks - to provide parking availability forecasts for extended prediction horizons. Two different types of predictions are provided: i. the probability of a free space to continue being free in subsequent time intervals, and ii. the short-term parking occupancy prediction in selected regions of an urban road network. The available data comes from a wide network of parking sensors installed on-street in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions and, then, survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a space continuing to be free in the forthcoming time intervals. Simple genetically optimized Multilayer Perceptrons accurately predict region parking occupancy up to 1 hour in the future by only exploiting 5 minute data. Finally, the real time, web based, implementation of the proposed parking prediction availability system is presented. Keywords: Parking occupancy, parking duration, parking sensors, smart city, internet of things



INTRODUCTION AND BACKGROUND Parking availability and provision are among the most important factors affecting both private car based trip decisions and traffic conditions in urban areas. Driver decisions are temporally dependent, implying that they are influenced by past experience, as well as real-time (on road) perceptions. Parking is such a case where prior knowledge on possible prevailing conditions (e.g. difficulty in finding a parking space, off-street parking costs, and so on) affects parking decisions. At the same time, vehicles in search of vacant parking spaces negatively impact traffic conditions and the environment. In such a context, parking information provision is a research area of particular interest since modern communication technologies offer alternative ways of delivering information to travelers in a timely and effective manner.

Systems developed for providing parking information and guidance have been proposed by researchers in the past (1-4); in such systems, information is usually disseminated by variable message signs or through the internet, cellular phones, PDA, and GIS technologies (2, 4). Teodorovic and Lucic (5) argue that although parking guidance systems may not affect the occupancy rate or average parking duration, drivers tend to greatly appreciate the information provided by such systems. This is the case since such systems significantly increase the probability of finding vacant parking spaces, mitigate frustration of those drivers/visitors unfamiliar with the city center, decrease queues in front of parking garages, decrease total vehicle-miles travelled (particularly in the city center), decrease average trip time, energy consumption, and air pollution.

The usefulness of such predictive parking information is straightforwardly understood. If all drivers act without information and make “uninformed” choices, they will probably resort to similar optimal decisions leading to induced long waiting times, queues and increased parking circling. On the other hand, dissemination of accurate and timely parking availability information may lead to improved driver decisions and parking searching (6). From a transport operator perspective, accurate parking availability forecasts may lead to better management of the system, congestion mitigation due to queue formation avoidance, and so on.

Parking modeling has been a topic of interest since the 1970s. An early review by Young et al. (7) reported three categories of parking related models: driver behavior with respect to parking (parking choice), optimal positioning of parking lots (parking allocation), and interaction of parking operations with other transportation system elements and infrastructures (parking interaction). A decade later, Arnott and Rowse (8) indicated that parking has been investigated from several perspectives including parking patterns, impacts on traffic, off-street parking technologies, parking policy, choice and location models, as well as economic models describing parking conditions. Parking availability prediction on the other hand, was introduced in recent years, coupled with modern capabilities on data collection and processing, along with ITS based exploitation of such information (a fact already indicated since the early 1990s (9)). In the early 2000s, David et al. (10) proposed a model for event-oriented forecasting of parking occupancy based on standardized daily distribution occupancy rate curves and on-line data obtained from parking lots equipped with detectors. On-street occupancy estimation in the absence of detectors was investigated by David and Keller (11). An event driven model was developed for that purpose using historical socioeconomic and parking specific data; the model was successfully validated for the city of Munich, Germany. Teodorovic and Lucic (5) developed a system based on fuzzy logic, simulation and optimization models, which decides whether to accept or reject new parking requests in real-time, according to estimated availability for



parking lots. Martens and Benenson (12) exploited agent-based modeling for representing parking behavior by integrating the effect of considering real-time and expected parking availability, prices, and parking enforcement. Caicedo (13) developed a discrete choice model for combining on-line and historical data for real-time, off street parking availability prediction; this model was later used by Caicedo et al. (3). A fuzzy logic model for estimating the uncertainty of peak period parking availability in park-and-ride facilities was proposed by Chen et al. (14). Recently, Fubuyasi et al. (15) developed ParkPGH, a system for predicting parking availability in eight Pittsburg parking facilities using historical and real-time data.

Modern technological advances have revolutionized the ways of monitoring and recording transportation operations and data; these have, however, been exploited for parking prediction to a lesser extent, and have mainly focused on off-street parking facilities. The objective of this paper is to exploit statistical and computational intelligence methods for developing a methodology that can be used for multiple step ahead on-street parking prediction in “smart” urban areas. This work takes advantage of massive real-time parking data availability, obtained by an extended parking sensor network available in the “smart” city of Santander, Spain. Models are developed for predicting expected parking occupancy along with the probability of finding vacant parking spaces. Traditional survival analysis models as well as neural network models are developed. The methodology is evaluated and a real-time, web based system exploiting the proposed prediction models, for the city of Santander, Spain, is presented.

SMART CITIES AND PARKING PREDICTION CHALLENGES Cities are characterized as “smart” when their transportation and communication (ICT) infrastructures along with their human and social capital investments cooperate and actively support sustainable growth and high quality of life, through participatory action and engagement while preserving natural resources (16). Indeed, as noted by Komninos (17), innovation and use of ICT for improving capacity of infrastructures are key elements of “smart” cities. A novel type of “smart” city infrastructure, applicable to the transportation sector is the so-called Internet of Things (IoT). IoT consists of a variety of devices or objects – such as Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, and so on – which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to reach common goals (18). By continuously collecting, analyzing and redistributing transportation information, IoT networks can offer valuable, real time information to both travelers and operators, and thus support and improve the operations of ITS, traffic and public transportation systems. Although one can trace several reasons that may prevent IoT to be fully developed in urban environments, this unique technological paradigm is expected to substantially support sustainable development of future smart cities (19).

The SmartSantander project is such a case of an IoT architecture deployed in the city of Santander, to achieve a massive deployment of sensors and network communications in order to provide efficient and equitable transportation and other services to citizens (http://www.smartsantander.eu/). The IoT network of the SmartSantander project consists of (20): IoT nodes: these are responsible for sensing and collecting information from the

natural and socioeconomic environment and activities (temperature, CO, noise, light, car presence etc.).



Repeaters: These nodes are placed high above ground in street lights, semaphores, information panels, etc, and behave as forwarding nodes of Iot information.

Gateways: Both IoT nodes and repeaters are configured to send all the information to gateways. Once information is received by gateways it can be either stored locally or sent to central processing units through different interfaces (for example, WiFi, GPRS/UMTS or Ethernet).

SmartSantander and similar testbeds fuse high resolution datasets stemming

from both static and mobile sensors. Such data refer to macroscopic traffic flow in road sections of interest, detailed parking information in real-time in urban areas, as well as high resolution transit information. In the case examined in this paper, having parking sensors collecting real time information on their occupancy provides accurate high resolution parking information. New technologies can replace common parking metrics of average duration, turnover rate and occupancy in extended time windows of 1 to 3 hours (which are manually collected in small scale regions) with accurate, both aggregate and disaggregate information, on occupancy and parking duration. In this framework there are several questions that may arise; this paper focuses on the following:

Is it possible to predict parking occupancy using time series modeling approaches based on data collected from an IoT network of sensors?

How accurate are parking occupancy predictions produced by such models in relation to the predictive horizon?

What are the statistical properties of parking space duration and how can we predict the probability of having free parking spaces in the area of interest?

MODELING PARKING AVAILABILITY BASED ON SENSOR DATA Methodology The architecture of the proposed parking occupancy prediction system is presented in FIGURE 1. In each sliding time window T , parking efficiency is defined by the following metrics:

Duration ( tD ) of free parking space: The time period that a slot is free,

Occupancy ( tO ): The percentage of parking slots occupied during a predefined

time period.

The free space duration is an indication of how frequently a parking space becomes available, whereas occupancy relates the parking accumulation, meaning the number of parked vehicles in the study area at any specific time period, to the parking capacity.

The proposed methodology has two modules. The first module is a real-time time series occupancy prediction scheme based on recurrent artificial neural networks. Simple yet flexible memory mechanisms will be applied in order to replicate the temporal dynamics of parking occupancy tO . The model is presented with past

information of occupancy ( ( 1),...,t t mO O ) to predict occupancy in one step ahead tO

. The characteristics of the model’s memory (the time delay τ and the dimension m) will be evaluated through a nonlinear analysis of the dynamics of parking occupancy. The use of exogenous variables such as traffic volume will be evaluated relative to the


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depicted using various graph methods (such as heat maps), to provide useful information to users. Neural Network Models for Time Series Prediction Time series modeling is a popular approach for making predictions in transportation problems (24). This approach is suitable for analyzing parking occupancy due to the temporal structure of the performance measures of parking systems. A common prediction strategy implemented in transportation problems is based on the autoregressive moving average family of models. These models are relatively straightforward mathematically and easy to produce; however, they are severely constrained by stationarity and linearity, characteristics that most frequently violated in real transportation time series. Treating non-stationarity and nonlinearity may lead to a tedious process without achieving the desired levels of accuracy in predictions and modeling reliability (25). Neural Networks (NNs) for time series provides a good alternative, as they relax many of these constraints (25), and also appear to provide short-term forecasting models that are more adaptable to sudden shifts in the data (27). A recent study on traffic time series prediction has shown the structural equivalencies between nonlinear univariate and multivariate ARIMA models with exogenous variables and dynamic forms of Multilayer Perceptrons (MLPs) (27). The simplest of all is the NAR(p) structure of order p. In general, The MLP presented with p lagged values of parking occupancy Ok ( k=1,…,n) may act as a predictor of the form (28):

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(usually back-propagation). For a time series consideration in a NN framework, the MLP should be

modified to account for the time sequence of events under study. This is usually accomplished by adding memory structures in than MLP that retain the effect of past information to the system and use it during learning. The memory is accomplished using local - at an intra-neuron level - and global - between neurons of different layers - recurrent connections in a neural network. Memory mechanisms may be of a simple tap delay form realizing structures:

( ) ( ),..., ( ( 1) )t O t O t m O (2)

where τ is the delay and m the dimension of the horizon of past information introduced to the model or of other more complex mathematical forms (Gamma memory and so on). The networks with memory usually require cumbersome and slow learning procedures that may not be always stable (27). To avoid this, static MLPs can be externally modified to represent the temporal characteristics of transportation time series (e.g. parking occupancy) in a manner resembling the common statistical prediction techniques. The introduction of such data inputs in



MLPs that are unchanged in their internal structural logic may conceptually approximate very complex and multivariate statistical structures with equal efficiency as classical MLPS. Hazard-Based Free Parking Duration Modeling Hazard-based duration modeling deals with the statistical representation of time to event data, a very frequent form of data in transportation problems; typical example of such transportation data is the time to clear and incident (29), (30), the time until the end of congested phenomena (31),(32), the time until the end of transit vehicle repair (33), the time to an activity (34), (35), the time to household evacuation under the emergence of a physical disaster (36), the time for a pedestrian to cross a signalized intersection approach (37), the time to vehicle transaction (38) and so on. Extensive review on the hazard based transportation modeling applications may be found in Hensher and Mannering (39) and Bhat (40). Moreover, methodological, computational and estimation issues in duration modeling with focus on transportation problems may be found in Washington et al. (25).

Parametric hazard-based modeling is based on two concepts: the survival function and the hazard function. Let T be a non-negative random variable representing the time a vehicle occupy a space, the survival function is defined as the probability that T is of length at least t (i.e. a parking space is occupied at least t min) and is given by:

( ) ( ) 1 ( ), 0 ,S t P T t F t t (3) where ( )F t is the cumulative probability. The survival function ( )S t can have a variety of shapes following certain restrictions; it is bounded by 0 and 1 and, as T cannot be negative, S(0) = 1. Moreover, the larger the t, S never increases (and usually decreases).

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0( ) ( )exp( )h t h tX βX (7)

where 0 ( )h t is the baseline hazard rate, β is a vector of estimated parameters and X is a vector of covariates.


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units) is also optimized using genetic algorithms. The model’s look back time window is also genetically optimized. The available data is divided into the training (60%), cross-validation (20%) and test set (20%) used for training the models, evaluating the training of the models, and testing the generalization power of the models, respectively. The genetic optimization showed that an MLP of 8 hidden layers and a look back time window of 5 minutes in the past may be efficiently used to predict parking occupancy (%) up to 60 steps in the future with high accuracy. Prediction results (test set) are seen in TABLE 2 with respect to the following forecasting metrics:

Mean Absolute Error (MAE): î i

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where iy and îy is the actual and predicted value with 1,..,i N , I is the time step, N is the number of samples, iy is the last known value relative to the prediction step. Results seem encouraging particularly for longer prediction horizons. For example, the models are able to predict each region’s parking occupancy 15 minutes into the future with less than 4.2% MAPE. TABLE 2: Results for different prediction horizons (prediction step τ equals to 1 minute) for each parking region (test set).

Prediction Horizon Region 1 1 min 5 min 15 min 30 min 60 min

MAE 0.011 0.028 0.034 0.036 0.037 RMSE 0.018 0.036 0.043 0.045 0.047 MAPE 1.374 3.437 4.188 4.377 4.490 RRSE 92.057 95.666 71.883 60.379 54.060

Region 2 MAE 0.007 0.010 0.012 0.013 0.014 RMSE 0.010 0.013 0.015 0.017 0.017 MAPE 0.816 1.103 1.312 1.449 1.488 RRSE 76.843 71.500 75.652 76.849 61.916




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Parking monitoring and short-term parking occupancy prediction. The architecture of the MITOS platform is seen in FIGURE 8. The services and applications deployed and demonstrated in the context of MITOS include a Web portal for transportation information, which is used as a city guide for citizens and tourists, and a mobile application for advanced transportation services. The last provides all type of information required by commuters and enables intuitive ways for searching routes and geo-referenced information based on the personal preferences and abilities of each user.

FIGURE 8: Architecture of MITOS platform.

An added value of the MITOS platform comes from the exploitation of various diverse sensor sources such as: Participatory sensors: users will act as sensors that provide relevant traffic and

travel information in the form of free text or predefined messages (e.g. “heavy traffic”, “too much noise”, etc.) and/or image.

Environmental data sensors, measuring noise, temperature and CO/ CO2 emissions On-street parking space occupancy sensors. Traffic occupancy sensors On-vehicle devices (GPS) installed in buses.

The implementation of a parking prediction service involves retrieving parking sensor data every minute (through the SmartSantander framework APIs) and storing them in a relational database (MITOS DB). Next, the algorithms described earlier are executed, and a continuous stream of predictions is generated. Those predicted values are stored back in the database, so that related user requests on parking availability can be served as fast as possible. Following user requests for parking occupancy, data are retrieved and visualized on a digital Web map (FIGURE 8) and on a mobile phone (FIGURE 9). The visualization involves data easily perceived by the end users, such as estimated occupancy and estimated number of free parking slots in the short-term future (5, 10 and 15 minutes ahead).


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CONCLUSIONS Driver decisions increasingly rely on technologies and information services that deliver high resolution transportation information. Parking availability prediction is a critical problem that has been recently investigated by researchers with the use of advanced algorithms combined with novel technological capabilities in data collection and processing. In this paper we exploited high resolution parking occupancy data and developed and tested a system for short and longer term parking availability prediction in urban areas.

The system encompasses two modules; the first applies survival analysis to predict the probability that a parking space will be free in the following time intervals. The second module introduces neural networks for the prediction of the time series of parking occupancy in different regions of an urban network. Findings show that the duration of free parking space follows a Weibull distribution. Moreover, the neural networks adequately captures the temporal evolution of parking occupancy and may accurately predict occupancy up to one hour ahead. The system is successfully incorporated within an innovative routing service (MITOS) for Santander, Spain, using real time data from the city’s “smart” infrastructure. ACKNOLEDGEMENTS

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