Proactive Vehicle Re-routing Strategies for Congestion Avoidance

Pan, Juan; Khan, Mohammad Arifin Rahman; Popa, Iulian Sandu; Zeitouni, Karine; Borcea, Cristian

doi:10.1109/dcoss.2012.29

Cited by 87 publications

(79 citation statements)

References 11 publications

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“…At the end of every step, the performance of each agent is assessed and the micro-simulator modifies the plans of the most problematic agents (e.g., by assigning a new path or modifying the departure time). Consequently this iterative nature might results in a high computational cost, in particular when the number of agents is large and/or the road network is complex [27]. Furthermore these equilibrium approaches rely on strong assumptions and have several limitations now well identified [28].…”

Section: Traffic Micro-simulatormentioning

confidence: 99%

“…Note that as only people over 15 years old were interviewed in the liveability survey, TransMob is able to calculate liveability and satisfaction index only for individuals over 15 years of age in the simulation. As Figure 16 shows, the majority of the population in all travel zones in the study area are between 30 and 64 years old, while younger group of people (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) and older group of people have approximately equal proportions. People above 65 years old appear to be more satisfied with their travel zones compared to people between 30 and 64 years old, who in turn, appear to be more satisfied with the people in the youngest age group.…”

Section: Scenario 1-base Linementioning

confidence: 99%

See 1 more Smart Citation

Simulating Transport and Land Use Interdependencies for Strategic Urban Planning—An Agent Based Modelling Approach

Huynh¹,

Perez²,

Berryman³

et al. 2015

Systems

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Agent based modelling has been widely accepted as a promising tool for urban planning purposes thanks to its capability to provide sophisticated insights into the social behaviours and the interdependencies that characterise urban systems. In this paper, we report on an agent based model, called TransMob, which explicitly simulates the mutual dynamics between demographic evolution, transport demands, housing needs and the eventual change in the average satisfaction of the residents of an urban area. The ability to reproduce such dynamics is a unique feature that has not been found in many of the like agent based models in the literature. TransMob, is constituted by six major modules: synthetic population, perceived liveability, travel diary assignment, traffic micro-simulator, residential location choice, and travel mode choice. TransMob is used to simulate the dynamics of a metropolitan area in South East of Sydney, Australia, in 2006 and 2011, with demographic evolution. The results are favourably compared against survey data for the area in 2011, therefore validating the capability of TransMob to reproduce the observed complexity of an urban area. We also report on the application of TransMob to simulate various hypothetical scenarios of urban planning policies. We conclude with discussions on current limitations of TransMob, which serve as suggestions for future developments.

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Section: Traffic Micro-simulatormentioning

confidence: 99%

Section: Scenario 1-base Linementioning

confidence: 99%

Simulating Transport and Land Use Interdependencies for Strategic Urban Planning—An Agent Based Modelling Approach

Huynh¹,

Perez²,

Berryman³

et al. 2015

Systems

View full text Add to dashboard Cite

show abstract

“…Travel time prediction algorithms attempt to estimate the time of travel between an arbitrary origin-destination pair in a roadway network, for current and future instances. It assists the driver to choose a less congested route, thus can be used for optimal routing and dynamic route guidance [14,15]. There is also a model to estimate the travel time that begins at a long-term future moment of departure [16].…”

Section: Related Workmentioning

confidence: 99%

En-Route Vehicular Traffic Optimization

Saravanan¹,

M²

2015

ijacsa

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Abstract-The pathways of information are changing, the physical world itself is becoming a type of information system. In what's called the Internet of Things (IoT), sensors and actuators embedded in physical objects-from roadways to pacemakersare linked through wired and wireless networks, often using the same Internet Protocol (IP) that connects the Internet. When objects can both sense the environment and communicate, they become tools for understanding complexity and responding to it swiftly. The revolutionary part in all this is that these physical information systems are now beginning to be deployed, and some of them even work largely without human intervention. This paper has addressed the traffic congestion problem with the help of Internet of Things. Increase in the number of vehicles in cities caused by the population and development of economy, has stimulated traffic congestion problems. It is becoming more serious day after day in the present scenario of developing countries. The reason for the same could be categorized as mismanagement of vehicular movement, ineffective system for controlling the mobility of vehicles, uneven roads and traffic snarl-up. Unexpected vehicular queuing is a major concern leading to wasting time of passengers and thwarting ambulance to reach the destination in time. In addition to that, traffic congestion makes it difficult to forecast the travel time accurately causing drivers to allocate more time in travel than scheduled previously. To ease these mounting traffic problems a demonstration is made on the Proof of Concept (POC) using the smart city data set provided by Telecom Italia of Milan city, to verify that these concepts have the potential for real world application and could be used by the government sectors or private transport organizations to ameliorate the passenger's comfort on road which are as follows. A central node is developed which sets the speed limit and predicts a normalized speed separately for each locality from the available data set. For efficient control in mobility of vehicles an advanced dynamic digital board is introduced, which displays the speed limit set by the central node time to time. The normalized speed could be used to estimate the effective time taken between destinations precisely. By comparing normalized speed with real time values anomalies in the locality like congestion and presence of uneven roads is predicted. Accident detection model is integrated with the central node which sends a message to dynamic board indicating location of the accident along with the time taken. It even improves traffic flow around the accident occurred location.Central node together with navigation tools could provide rerouted path to the drivers during congestion or accident.

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“…The congestion avoidance in [21] relies on the identification of congestion-freedom for an alternative path in the global point of view. However, the delay problem can still be there when this backup path is blocked by another congestion newly emerged.…”

Section: Related Workmentioning

confidence: 99%

GUI: GPS-Less Traffic Congestion Avoidance in Urban Areas with Inter-Vehicular Communication

Jiang

Sabatino

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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Abstract-Driving in an urban canyon can be frustrating when your GPS teller keeps telling you to make a turn at the place that you just passed, because the information transmission is deferred by the wireless signal reflecting off of buildings and other interfering objects. In this paper, we provide a practical solution for turn-to-turn guidance with inter-vehicle communication in vehicle ad-hoc networks (VANETs). Vehicles collect information from neighbors and catch the snapshot to describe the global impact of traffic congestions, in the presence of unpredictable changes of topology and vehicle trajectory. Without any centralized control, the information can be aggregated along the traffic flow and be disseminated in a minimal area, while sufficiently guiding each vehicle to achieve a global optimization on its path, and to remain on a non-blocked route. The information constitution is implemented in the proactive model, saving the delay of reconstruction in the reactive model (on-demand). Its substantial improvement on the elapsed time will be shown in the experimental results, compared with the best results known to date in both proactive and reactive information models.

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Proactive Vehicle Re-routing Strategies for Congestion Avoidance

Cited by 87 publications

References 11 publications

Simulating Transport and Land Use Interdependencies for Strategic Urban Planning—An Agent Based Modelling Approach

Simulating Transport and Land Use Interdependencies for Strategic Urban Planning—An Agent Based Modelling Approach

En-Route Vehicular Traffic Optimization

GUI: GPS-Less Traffic Congestion Avoidance in Urban Areas with Inter-Vehicular Communication

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