A Hyperpath-based Network Generalized Extreme-value Model for Route Choice under Uncertainties

Ma, Jiangshan; Fukuda, Daisuke

doi:10.1016/j.trpro.2015.06.003

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…The hyperpath routing approach addresses uncertainty and variability of traffic dynamics, where individual traffic agents receive for each origin and destination a tree of alternatives, as opposed to a single route [ 10 , 11 , 13 , 32 ]. It feeds off historical data to discern traffic behavior patterns [ 12 , 33 , 34 ], thus requiring heavy back-end computing to synthesize the pre-trips. TWM is complementary to hyperpaths as it provides different network views for hyperpath calculation.…”

Section: Related Workmentioning

confidence: 99%

“…There are many future research possibilities that have been pointed out in the paper: (a) creating optimal static TWM distributions for historical data with evolutionary algorithms, (b) creating dynamic traffic assignment models using TWM based on previous driver experiences as we have shown that they may be critical for TWM adherence, (c) using TWM for zone routing policies as pointed by [ 49 ], (d) applying TWM to hyperpath calculation using techniques described by [ 12 , 34 ], (e) creating evolutionary algorithms and optimization functions for finding local area minimum for routing maps that can cover eventual time-dependent situations, and (f) extension of the MuTraff MTS simulator with mesoscopic simulation engines.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

“…Route diversification is a key element to deliver resources in an efficient way. Several mechanisms have been proposed to deal with it: Centralized ones use hyperpaths and K-shortest-hyperpath variations to obtain route diversification [ 10 , 11 , 12 , 13 ]. On the other hand, distributed mechanisms provide ad-hoc local routing based on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications but they assume high computing and ideal connectivity capabilities in the vehicles and the infrastructure [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.

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Section: Related Workmentioning

confidence: 99%

Section: Conclusion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Paricio

López-Carmona

2019

Sensors

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“…In these situations, some drivers would make risky decisions, while others would make risk-averse decisions. The behavioral differences among drivers with uncertainties and risks could produce different route choice ( 24 – 27 ).…”

Section: Congestion Pricingmentioning

confidence: 99%

Development of Traffic-Based Congestion Pricing and Its Application to Automated Vehicles

Lee

Kockelman

2019

Transportation Research Record: Journal of the Transportation R

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Improved traffic management techniques are needed to reduce congestion on road networks, especially as “driving” is made easier, through self-driving vehicles. In this paper, reactive congestion pricing varies toll rates based on recent congestion levels, and automated vehicles are added to the conventional traffic mix for evaluation of evolving travel conditions. As expected, drivers with higher values of travel time (VOTT) are more likely to use the tolled route than drivers with lower VOTT, and tolled-route speeds rose (about 4%) while speeds on non-tolled road segments fell (about 15%). Thanks to traveler sorting, net benefits exceeded $600 per hour in all scenarios, using a very small (toy) network. Toll revenues can be distributed uniformly among travelers (resulting in credit-based congestion pricing) or invested in improving bottlenecks and alternative modes. Rising shares of automated vehicles (from 0% to 50% and 100%) also improved outcomes.

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“…They found that with a threshold of 80% to define the same route, a combination of link elimination and simulation method can identify 92% of the route people actually use. Ma and Fukuda [ 35 ] compare shortest path routes of taxis with hyperpaths and find that hyperpaths have more explanatory power. GPS data from Taxis may not be consistent with general traffic.…”

Section: Introductionmentioning

confidence: 99%

Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle

2015

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Most recent route choice models, following either the random utility maximization or rule-based paradigm, require explicit enumeration of feasible routes. The quality of model estimation and prediction is sensitive to the appropriateness of the consideration set. However, few empirical studies of revealed route characteristics have been reported in the literature. This study evaluates the widely applied shortest path assumption by evaluating routes followed by residents of the Minneapolis—St. Paul metropolitan area. Accurate Global Positioning System (GPS) and Geographic Information System (GIS) data were employed to reveal routes people used over an eight to thirteen week period. Most people did not choose the shortest path. Using three weeks of that data, we find that current route choice set generation algorithms do not reveal the majority of paths that individuals took. Findings from this study may guide future efforts in building better route choice models.

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A Hyperpath-based Network Generalized Extreme-value Model for Route Choice under Uncertainties

Cited by 7 publications

References 37 publications

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

RETRACTED: MuTraff: A Smart-City Multi-Map Traffic Routing Framework

Development of Traffic-Based Congestion Pricing and Its Application to Automated Vehicles

Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle

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