Wireless Communication Alternatives for Intelligent Transportation Systems: A Case Study

Zhou, Yan; Evans, Glenn Hamilton; Chowdhury, Mashrur; Wang, Kuang-Ching; Fries, Ryan

doi:10.1080/15472450.2011.594681

Cited by 14 publications

(13 citation statements)

References 8 publications

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“…Therefore, a fast, reliable and cost‐effective communication system to collect and disseminate real‐time traffic information is of paramount importance. Key technical factors are not clearly understood by transportation agencies, such as actual performance of different wireless options in the field when supporting various kinds of traffic control devices [5]. To assess the reliability and adequacy of the wireless communication infrastructure, researchers must evaluate and model different alternate communication technologies and corresponding network performance.…”

Section: Introductionmentioning

confidence: 99%

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

Zhou

Dey

Chowdhury

et al. 2016

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

Zhou

Dey

Chowdhury

et al. 2016

IET Intelligent Transport Systems

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“…Real‐time vehicle routing with sufficient information can improve the accuracy of departure time and travel time prediction by quickly responding to uncertainties caused by accidents, congestions, and lane closures. Once the real‐time communication capability among vehicles and infrastructure is enabled using VII technologies [16, 17], the optimal path will be built in an ongoing fashion at each time when a vehicle is approaching a decision node with more than one route to follow. To give the recurring updates real‐world relevance, real‐time vehicle routing must incorporate a fast and efficient algorithm to obtain the optimal result within a limited time.…”

Section: Introductionmentioning

confidence: 99%

Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

Li¹,

Dey²,

Chowdhury³

et al. 2016

IET Intelligent Transport Systems

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Electric vehicle (EV) charging problem impedes its wide scale commercial adoption. In this study, the authors address this problem through an ant colony optimisation based multiobjective routing algorithm that is dedicated to accommodate EV trips. By using connectivity, EVs communicate with other vehicles and infrastructure components to transmit information in real time for finding the best route, and for intelligently recharging on the move using an inductively coupled power transfer system. Such connected EVs are capable of adapting each trip with the lowest travel time and/or the lowest recharge cost along with an optimal recharge plan to prevent a battery drain. As a case study, a real world roadway network in Charleston, South Carolina was simulated to examine the performance of the routing strategy. Simulation analysis revealed that connected EVs can reduce not only the total travel time and the energy consumption, but also the recharged volume of electricity and corresponding cost, thus significantly relieving the concerns of range anxiety of EV drivers. This routing approach potentially leads to a reduction in the EV battery capacity requirement, which in turn can reduce the cost of energy storage systems to a reasonable level.

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“…Wie et al [24], Friesz and Mookherjee [25], and Chow [20] analyzed theoretically the dynamic traffic assignment problem with departure time choice. Wie et al formulated the user equilibrium and system optimum conditions and compared the two using a numerical example.…”

Section: Departure Time Choice Studiesmentioning

confidence: 99%

“…MATSim is an open-source software that provides a framework to implement large-scale agent-based transportation simulations [21]. It has been applied by numerous researchers in traffic impact analysis, road/congestion pricing analysis, carpooling, freight modelling, environment effect evaluations, and evacuation plans [22][23][24][25][26][27]. Agent-based simulation is rather effective when a process includes numerous heterogeneous participants interacting with each other in a decentralized way.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These studies showed promising results. (Wang & Lin, 2008) as the simulation tool from many existing commercial/non-commercial communication-network simulators such as NS-2 [24], OPNET [25], GlomoSim [26], NCTUns [27], because NCTUns, at the time of this study, does implement most of the DSRC protocol layers without modifying the source code.…”

Section: Chapter Calibrating Communication Simulator For Connected Vementioning

confidence: 99%

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Highway Reservation System: Models, Simulations, and Implementation Discussions

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Motivation Urban road networks are severely congested with economic development, resulting in increased travel times, air pollution, noise, and traffic crashes. As shown by the National Congestion Measures between 1982 and 2011, provided by the 2012 Urban Mobility Report [1], congestion has increased substantially over the 30 years, and is "recovering" from the economic recession during 2009. The delay per commuter in 2011 is 38 hours, which is equivalent to 5 days of vacation per year. Adding more capacity by providing more road lanes and more public transportation is the most fundamental congestion solution in most growing urban regions to satisfy the increasing travel demand. However, road capacity almost always increases in a slower rate than the demand growth. As shown by the Road Growth and Mobility Level Exhibit [1] (Figure 1), 56 in 101 study areas have travel demand growth 30% faster than supply, and only 17 areas have a less than 10% gap between demand and supply growth. Furthermore, accidents or work zones may create bottlenecks on the road and seriously downgrade the road capacity. It is not economically justified and hardly possible to satisfy the increasing peak hour demand. An economical way is leveling the time fluctuation of traffic demand in order to decrease the imbalance between road capacity and traffic demand during the peak-hours.

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Wireless Communication Alternatives for Intelligent Transportation Systems: A Case Study

Cited by 14 publications

References 8 publications

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

Process for evaluating the data transfer performance of wireless traffic sensors for real‐time intelligent transportation systems applications

Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

Highway Reservation System: Models, Simulations, and Implementation Discussions

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