Adaptive route choice model for public transit systems: an application of Markov decision processes

2012 ◽  
Vol 39 (8) ◽  
pp. 915-924 ◽  
Author(s):  
Behzad Rouhieh ◽  
Ciprian Alecsandru
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Public Transit ◽  
Choice Model ◽  
Transportation Systems ◽  
Transit System ◽  
Transit Systems ◽  
The Impact

Over the past couple of decades the advancements in the areas of information and computational technology allowed for a variety of intelligent transportation systems developments and deployments. This study investigates an advanced traveler information system (ATIS) and (or) an advanced public transit system (APTS) adaptive and real-time transit routing component. The proposed methodology is applied to bus routes with fixed, predefined bus line alignments. It is shown that routing buses on such systems can be modeled in real-time by employing an associated Markov chain with reward model to minimize the impact of congested traffic conditions on the travelers and the overall operation cost of the transit system. A case study using a traffic and transit data from a real-world bus line was used to apply the proposed bus routing approach. It was found that under certain traffic congestion conditions buses should be re-routed to minimize their travel time and the associated system costs. The hypothetical congestion scenarios investigated show that individual bus travel time delays range between 50 and 740 s when the proposed adaptive routing is employed. The proposed methodology is also suitable for application to transit systems that run on a demand-adaptive basis (the bus line alignment changes with the travelers demand). Additional calibration and future integration of the system into specific ATIS and (or) APTS user services will be investigated.

Assessing the Existing Bursa Light Rail Transportation System

2020 ◽  
Vol 2674 (10) ◽  
pp. 250-260
Author(s):  
Mehmet Rizelioğlu ◽  
Turan Arslan
Keyword(s):  
Traffic Congestion ◽  
Transportation System ◽  
Transportation Systems ◽  
Bus Rapid Transit ◽  
Light Rail ◽  
Rail Transportation ◽  
Transit System ◽  
Transit Systems ◽  
Negative Impacts

As car ownership soars, traffic congestion and its associated negative impacts have become real concerns in many cities around the world. Therefore, transportation systems that perform better in eliminating or reducing traffic congestion and related problems to tolerable levels have become imperative. Alternative transit systems should be assessed properly to accommodate the expected demand in the long term, at least, to some significant extent. However, this is generally neglected in developing countries and, among many possible alternatives, a popular transportation system is usually preferred within the available budget. As an example, Bursa Metropolitan Municipality, Turkey, has recently implemented a light rail transportation system (LRT) on its major east–west corridor as the main transit system. In this study, the existing LRT is assessed and its performance is compared with a hypothetical bus rapid transit (BRT) system, which is a strong contender and comparatively a lower-cost alternative. This is done to assess whether the LRT was the better choice in relation to the current demand. Therefore, in this study, the existing LRT system is first defined in the PTV VISSIM simulation environment. Then, the hypothetical BRT system is considered on the same route with the current demand. The capability and capacities of the existing LRT and the BRT system are assessed and compared in many aspects. The results are compared, and important findings are outlined.

scholarly journals Real-Time Adaptive Traffic Signals Using Rate-Monotonic Scheduling

2019 ◽  
Vol 29 ◽  
pp. 03002 ◽  
Author(s):  
Mãdãlin-Dorin Pop
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Fixed Time ◽  
Transportation Systems ◽  
Real Time Scheduling ◽  
Traffic Lights ◽  
Rate Monotonic ◽  
Rate Monotonic Scheduling

The studies and real situations shown that the traffic congestion is one of nowadays highest problems. This problem wassolved in the past using roundabouts and traffic signals. Taking in account the number of cars that is increasing continuously, we can see that past approaches using traffic lights with fixed-time controller for traffic signals timing is obsolete. The present and the future is the using of Intelligent Transportation Systems. Traffic lights systems should be aware about realtime traffic parameters and should adapt accordingly to them. The purpose of this paper is to present a new approach to control traffic signals using rate-monotonic scheduling. Obtained results will be compared with the results obtained by using others real-time scheduling algorithms.

scholarly journals Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning

2020 ◽  
Vol 10 (13) ◽  
pp. 4541 ◽  
Author(s):  
Zahid Khan ◽  
Anis Koubaa ◽  
Haleem Farman
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Performance Metrics ◽  
Complex Structure ◽  
Transportation Systems ◽  
Congestion Avoidance ◽  
Internet Of Vehicles ◽  
Area Of Interest ◽  
Congestion Detection

Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has two broad parts: (1) IoV-based congestion detection (IoV-based CD); and (2) IoV-based congestion avoidance (IoV-based CA). In the given area of interest, the congestion detection phase sets a parametric approach to calculate the capacity of each entry point for real-time traffic congestion detection. On each road segment, the installed roadside unit (RSU) assesses the traffic status concerning two factors: (a) occupancy rate and (b) occupancy time. If the values of these factors (either a or b) exceed the threshold limits, then congestion will be detected in real time. Next, IoV-based congestion avoidance triggers rerouting using modified Evolving Graph (EG)-Dijkstra, if the number of arriving vehicles or the occupancy time of an individual vehicle exceeds the thresholds. Moreover, the rerouting scheme in IoV-based congestion avoidance also considers the capacity of the alternate routes to avoid the possibility of moving congestion from one place to another. From the experimental results, we determine that proposed IoV-based congestion detection and avoidance significantly improves (i.e., 80%) the performance metrics (i.e., path cost, travel time, travelling speed) in low segment size scenarios than the previous microscopic congestion detection protocol (MCDP). Although in the case of simulation time, the performance increase depends on traffic congestion status (low, medium, high, massive), the performance increase varies from 0 to 100%.

scholarly journals Route guidance strategies revisited: Comparison and evaluation in an asymmetric two-route traffic network

2014 ◽  
Vol 25 (04) ◽  
pp. 1450005 ◽  
Author(s):  
Zhengbing He ◽  
Bokui Chen ◽  
Ning Jia ◽  
Wei Guan ◽  
Benchuan Lin ◽  
...  
Keyword(s):  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Traffic Network ◽  
Route Guidance ◽  
Mean Velocity ◽  
The Past ◽  
Guidance Strategies ◽  
Alternative Routes

To alleviate traffic congestion, a variety of route guidance strategies have been proposed for intelligent transportation systems. A number of strategies are introduced and investigated on a symmetric two-route traffic network over the past decade. To evaluate the strategies in a more general scenario, this paper conducts eight prevalent strategies on an asymmetric two-route traffic network with different slowdown behaviors on alternative routes. The results show that only mean velocity feedback strategy (MVFS) is able to equalize travel time, i.e. approximate user optimality (UO); while the others fail due to incapability of establishing relations between the feedback parameters and travel time. The paper helps better understand these strategies, and suggests MVFS if the authority intends to achieve user optimality.

scholarly journals Subways, Strikes, and Slowdowns: The Impacts of Public Transit on Traffic Congestion

2014 ◽  
Vol 104 (9) ◽  
pp. 2763-2796 ◽  
Author(s):  
Michael L. Anderson
Keyword(s):  
Los Angeles ◽  
Traffic Congestion ◽  
Regression Discontinuity ◽  
Public Transit ◽  
Choice Model ◽  
Public Support ◽  
Regression Discontinuity Design ◽  
Transit Systems ◽  
Transit Service ◽  
Net Benefits

Public transit accounts for 1 percent of US passenger miles traveled but attracts strong public support. Using a simple choice model, we predict that transit riders are likely to be individuals who commute along routes with severe roadway delays. These individuals' choices thus have high marginal impacts on congestion. We test this prediction with data from a strike in 2003 by Los Angeles transit workers. Estimating a regression discontinuity design, we find that average highway delay increases 47 percent when transit service ceases. We find that the net benefits of transit systems appear to be much larger than previously believed. (JEL H76, J52, L92, R41)

scholarly journals Performance measurement and operations control in advanced public transit systems

2021 ◽  
Author(s):  
Nader Azizi
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Public Transit ◽  
Monitoring And Control ◽  
Real Time Control ◽  
Transit Systems ◽  
Transit Agencies ◽  
Real Time Information ◽  
And Control ◽  
Time Information

In most major cities, levels of traffic congestion are rising along with their associated problems such as travel delays and pollution. While any increase in public transit rider-ship could reduce the level of traffic congestion and related costs, most transit agencies are not able to expand their existing services because of fiscal• and physical constraints. As a result, a growing interest has been developing recently to maximize the transit system efficiency and productivity using new emerging technologies. Recently, the emergence of new technologies such as automatic vehicle location (AVL) and global positioning systems (GPS) has facilitated the design of computer-based real-time decision support systems for public transits. These technologies could significantly help transit agencies improve their operations monitoring and control. In the context of public transit systems, operations monitoring refers to real-time service performance measure and problems detection, and control refers to implementing real time control actions to remedy those problems. This thesis presents a new approach for operations monitoring and control in public transit systems with real-time information. First, an integrated model that combines both headway-based and schedule-based services is presented. To measure the headway or schedule adherence, the model uses predicted arrival times of vehicles at downstream stops. This feature allows the operational managers to avoid major service interruptions by proactively taking necessary corrective actions. Transit agencies have used and continue to use real-time control strategies to improve quality of their services. These strategies are employed by inspectors at various points along a route to remedy the problems as they occur. Practice shows that it is difficult to apply such strategies effectively without real-time information. In the second part of this thesis, a mathematical model for holding control strategy with real-time information is described. The proposed model aims at minimization of the total passengers waiting time and considers both cases of overcrowded and underutilized services. Due to complexity of the holding problem, several metaheuristics are proposed and tested. Among all intelligent search algorithms, a new version of simulated annealing algorithm is proposed to solve the real-time holding control model.

Performance measurement and operations control in advanced public transit systems

2021 ◽  
Author(s):  
Nader Azizi
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Public Transit ◽  
Monitoring And Control ◽  
Real Time Control ◽  
Transit Systems ◽  
Transit Agencies ◽  
Real Time Information ◽  
And Control ◽  
Time Information

In most major cities, levels of traffic congestion are rising along with their associated problems such as travel delays and pollution. While any increase in public transit rider-ship could reduce the level of traffic congestion and related costs, most transit agencies are not able to expand their existing services because of fiscal• and physical constraints. As a result, a growing interest has been developing recently to maximize the transit system efficiency and productivity using new emerging technologies. Recently, the emergence of new technologies such as automatic vehicle location (AVL) and global positioning systems (GPS) has facilitated the design of computer-based real-time decision support systems for public transits. These technologies could significantly help transit agencies improve their operations monitoring and control. In the context of public transit systems, operations monitoring refers to real-time service performance measure and problems detection, and control refers to implementing real time control actions to remedy those problems. This thesis presents a new approach for operations monitoring and control in public transit systems with real-time information. First, an integrated model that combines both headway-based and schedule-based services is presented. To measure the headway or schedule adherence, the model uses predicted arrival times of vehicles at downstream stops. This feature allows the operational managers to avoid major service interruptions by proactively taking necessary corrective actions. Transit agencies have used and continue to use real-time control strategies to improve quality of their services. These strategies are employed by inspectors at various points along a route to remedy the problems as they occur. Practice shows that it is difficult to apply such strategies effectively without real-time information. In the second part of this thesis, a mathematical model for holding control strategy with real-time information is described. The proposed model aims at minimization of the total passengers waiting time and considers both cases of overcrowded and underutilized services. Due to complexity of the holding problem, several metaheuristics are proposed and tested. Among all intelligent search algorithms, a new version of simulated annealing algorithm is proposed to solve the real-time holding control model.

A Comprehensive Study on Intelligent Transportation Systems

2018 ◽  
Vol 4 (10) ◽  
pp. 10
Author(s):  
Ankur Mishra ◽  
Aayushi Priya
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Environmental Conservation ◽  
Integrated System ◽  
Transport Systems ◽  
Transport Sector ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Transportation Sector

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

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