A model based approach to predict stream travel time using public transit as probes
05 Jun 2011-pp 101-106
TL;DR: In this paper, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in a pilot study, where only twowheeled vehicles have been considered as they constitute a major proportion in the study area.
Abstract: Travel time is one of the most preferred traffic information by a wide variety of travelers. Travel time information provided through variable message signs at the roadside could be viewed as a traffic management strategy designed to encourage drivers to take an alternate route. At the same time, it could also be viewed as a traveler information service designed to ensure that the driver has the best available information based on which they can make travel decisions. In an Intelligent Transportation Systems (ITS) context, both the Advanced Traveler Information Systems (ATIS) and the Advance Traffic Management Systems (ATMS) rely on accurate travel time prediction along arterials or freeways. In India, currently there is no permanent system of active test vehicles or license plate matching techniques to measure stream travel time in urban arterials. However, the public transit vehicles are being equipped with Global Positioning System (GPS) devices in major metropolitan cities of India for providing the bus arrival time information at bus stops. However, equipping private vehicles with GPS to enable the stream travel time measurement is difficult due to the requirement of public participation. The use of the GPS equipped buses as probe vehicles and estimating the stream travel time is a possible solution to this problem. The use of public transit as probes for travel time estimation offers advantages like frequent trips during peak hours, wide range network coverage, etc. However, the travel time characteristics of public transit buses are influenced by the transit characteristics like frequent acceleration, deceleration and stops due to bus stops besides their physical characteristics. Also, the sample size of public transit is less when compared to the total vehicle population. Thus mapping the bus travel time to stream travel time is a real challenge and this difficulty is more complex in traffic conditions like in India with its heterogeneity and lack of lane discipline. As a pilot study, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in the present study. Since it is only a pilot study, only twowheeled vehicles have been considered as they constitute a major proportion in the study area. The prediction scheme is corroborated using field data collected by carrying GPS units in two-wheelers traveling along with the buses under consideration. The travel time estimates from the model were compared with the manually observed travel times and the results are encouraging.
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TL;DR: In this paper, the authors proposed a method to predict stream travel time using particle filtering approach which considers the predicted stream travel times as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error.
Abstract: Travel-time information is an integral part of Advanced Traveler Information Systems and Advanced Traffic Management Systems. Real-time estimation of stream travel time will be helpful in making trip decisions such as route choice and departure time. The present study proposes a method to predict stream travel time using particle filtering approach which considers the predicted stream travel time as the sum of the median of historical travel times, random variations in travel time over time, and a model evolution error. The present model hypothesizes the median of historical travel times obtained from the collected data as a priori estimate and hence predicting the actual travel time will be equivalent to forecasting the variations in travel time according to the current measurements. In order to capture the random variations in travel time, a dynamic mathematical modeling approach with particle filtering technique is used. The results obtained from the implementation of the above method are compa...
18 citations
TL;DR: The conventional methodology of link travel time prediction to trip using historical trajectory data from taxis in urban road network is extended and experiments based on taxi data in Shenzhen are conducted, and the reliability of prediction results are evaluated.
Abstract: Travel time prediction could be applied to various fields and purposes. For traffic managers, travel time prediction is a fundamental part of traffic system operation. Its results may assist traffic management department in adjusting traffic flow through time-dependent rules. From the travellers' viewpoints, travel time prediction saves travel time and improves reliability through the selection of travel routes pre-trip and en route to optimize travel plans. A large number of research efforts on travel time prediction have been conducted, but trip travel time prediction is relatively limited, compared with link travel time. Travellers are more interested in specific trip travel time than average link travel time. The advances in positioning technologies, such as Global Positioning System (GPS), make it possible to collect a large number of vehicle trajectories which cover the whole road network as long as data are enough and is growing as an alternative data set for travel time prediction as well as other...
16 citations
TL;DR: A lumped parameter macroscopic traffic flow model has been formulated, and using this model, a model-based estimation scheme has been designed based on the Kalman filtering technique, and the performance was found to be satisfactory.
Abstract: One of the most popular intelligent transportation systems (ITS) applications is to provide real-time road traffic congestion information to the users. Traffic density is a major congestion indicator, and because its measurement is difficult, it is usually estimated from other readily measurable parameters. Several studies have explored various approaches for density estimation for homogeneous and lane-disciplined traffic conditions. However, Indian traffic is different with its heterogeneity of traffic and absence of lane discipline. Another characteristic is the lack of access control, making automated measurement of net entry into a study section difficult. An added difficulty is that the roads in India are not yet equipped with traffic sensors, leading to limitation in data collection. The present study mainly addresses the issue of estimating traffic density in the absence of automated sensors at the side roads/ramps on Indian roadways. A lumped parameter macroscopic traffic flow model has been formulated, and using this model, a model-based estimation scheme has been designed based on the Kalman filtering technique. The only data required for implementing this method in the field are the flow passing the entry location and the spot speeds of vehicles passing through the entry and exit locations. The proposed method was corroborated using data measured from a road stretch in Chennai, and the performance was found to be satisfactory.
12 citations
13 Sep 2020
TL;DR: This study explores the use of GPS data from buses and Wi-Fi and Bluetooth data from a sample of vehicles, for accurate estimation of the travel time of all vehicles on the roadway.
Abstract: Travel time information assists road users in making informed travel decisions such as mode choice, route choice and/or time of travel. This study explores the use of GPS data from buses and Wi-Fi ...
11 citations
TL;DR: The analysis of real-life data collected from bus GPS probes in Santiago, Chile indicates that GPS devices in transit buses can effectively provide the proposed performance measures throughout the route on a daily basis.
Abstract: Emerging GPS devices enable new data collection opportunities for transit performance monitoring. In addition to the fact that GPS devices can replace labor-intensive survey techniques, they also collect traffic information throughout transit routes that the traditional fixed loop detectors cannot. If public transit vehicles are equipped with GPS devices, it is possible to monitor the performance of public transit services throughout the routes and alert the associated authorities at significantly lower costs about potential problems for corrective actions to be taken. Transantiago, a major public transit service provider in Santiago, Chile, has recently installed GPS sensors on all its vehicles which provides an excellent venue on which an innovative transit monitoring methodology can be modeled and applied. This paper first conducts current-status analysis on distributions of headways throughout a route in Santiago by processing extensive raw GPS data from transit vehicles. Then, unique transit headway adherence indices are developed with respect to the expected passenger waiting time and are presented in forms of two-dimensional tempo-spatial graphs. The analysis of real-life data collected from bus GPS probes in Santiago, Chile indicates that GPS devices in transit buses can effectively provide the proposed performance measures throughout the route on a daily basis.
10 citations
References
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TL;DR: One of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions is presented, using global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India.
Abstract: Travel time information is a vital component of many intelligent transportation systems (ITS) applications. In recent years, the number of vehicles in India has increased tremendously, leading to severe traffic congestion and pollution in urban areas, particularly during peak periods. A desirable strategy to deal with such issues is to shift more people from personal vehicles to public transport by providing better service (comfort, convenience and so on). In this context, advanced public transportation systems (APTS) are one of the most important ITS applications, which can significantly improve the traffic situation in India. One such application will be to provide accurate information about bus arrivals to passengers, leading to reduced waiting times at bus stops. This needs a real-time data collection technique, a quick and reliable prediction technique to calculate the expected travel time based on real-time data and informing the passengers regarding the same. The scope of this study is to use global positioning system data collected from public transportation buses plying on urban roadways in the city of Chennai, India, to predict travel times under heterogeneous traffic conditions using an algorithm based on the Kalman filtering technique. The performance of the proposed algorithm is found to be promising and expected to be valuable in the development of APTS in India. The work presented here is one of the first attempts at real-time short-term prediction of travel time for ITS applications in Indian traffic conditions.
179 citations
TL;DR: The use of automatic vehicle location (AVL) data for characterizing the performance of an arterial is demonstrated and some conclusions are drawn regarding the utility of the transit AVL data.
Abstract: With the growing availability of data because of the deployment of intelligent transportation systems, methods for assessing and reporting traffic characteristics and conditions have begun to shift. Although previous level-of-service methods were developed for use with limited data, actual performance measures can now be developed and tested. On freeways, performance measures often are estimated directly by using data from inductive loop detectors (e.g., speed, occupancy, vehicle counts). For arterials with numerous signalized intersections, performance measures are more challenging because of more complicated traffic control and many origins and destinations. However, within signalized networks, travel time, speed, and other key performance measures can be obtained both directly and indirectly from sources such as automatic vehicle location (AVL) data. The use of AVL data for characterizing the performance of an arterial is demonstrated. First, data are extracted from the bus dispatch system of the Tri-County Metropolitan Transit District (TriMet), the transit provider for Portland, Oregon. Then, the performance characteristics as described by bus travel on an arterial are compared to ground truth data collected by probe vehicles equipped with Global Positioning System sensors traveling with normal (nontransit) traffic on the same arterial on the same days. Comparisons are made between the two methods, and some conclusions are drawn regarding the utility of the transit AVL data.
116 citations
TL;DR: The study suggests that the AVL-equipped transit vehicle can be used as a probe vehicle to collect travel time data at regular intervals with minimum cost.
Abstract: Obtaining near real-time information of travel times is a critical element of most applications of intelligent transportation systems. The use of transit vehicles as probe vehicles for collecting travel time data for automobiles on urban corridors was examined. Because transit vehicles are increasingly equipped with an automated vehicle locator (AVL) for reporting the current location of the vehicle, it may be possible to use the AVL data for travel time purposes. In anticipation of such an application of AVL, the relationship between travel times of a transit vehicle and of an automobile is examined for stability of data and adjustment needs. Travel times of transit vehicles and automobiles were measured simultaneously along the same sections on major corridors in Delaware. The difference in travel times was relatively stable, and, hence, appropriate formulas for predicting the travel time of automobiles were developed. The model coefficients were found to be reasonable and stable for various traffic conditions. The study suggests that the AVL-equipped transit vehicle can be used as a probe vehicle to collect travel time data at regular intervals with minimum cost.
95 citations
TL;DR: This study developed a methodology to mine the transit AVL data to find all trips that use any portion of a prespecified freeway segment, which is then used to measure travel time and average speed over the freeway and thereby quantify conditions on the facility.
Abstract: Many public transit agencies have equipped their fleet with automatic vehicle location (AVL) systems, which periodically provide the location of each vehicle in the fleet. Although the AVL is deployed for transit operations, the vehicles also provide valuable information about the traffic stream throughout the road network. This study developed a methodology to mine the transit AVL data to find all trips that use any portion of a prespecified freeway segment. These trips are then used to measure travel time and average speed over the freeway and thereby quantify conditions on the facility. The results are validated against concurrent loop detector data from a corridor. The greatest benefits, however, are in areas without fixed vehicle detection, so the methodology is also demonstrated on such a freeway corridor. The study corridors typically have fewer than 50 observations per day per kilometer per direction, so this paper includes a process for selecting those segments with at least one observation per h...
23 citations