Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities

Chiu, Yi Chang; Zhou, Liang; Song, Houbing

doi:10.1016/j.trb.2009.06.001

Cited by 63 publications

(48 citation statements)

References 26 publications

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“…A mesoscopic traffic simulation model for the study area is developed (Fig. 3) using DynusT package (DYNamic Urban Systems in Transportation, Chiu et al, 2010). The network includes around 2000 links, 500 nodes, over 300 signalized intersections, 201 TAZs, three major freeway corridors, and one tolling highway.…”

Section: Simulation Study Descriptionmentioning

confidence: 99%

“…In these simulation scenarios, real-time information is updated in each time period based on time-varying link travel time retrieved from the AMS model (Chiu et al, 2010). AMS is a vehicle-based mesoscopic traffic simulation approach that explicitly considers the anisotropic property of traffic flow into the vehicle state updated at each simulation period.…”

Section: Simulation Study Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

Xiong

Chen

et al. 2016

Transportation Research Part C: Emerging Technologies

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Section: Simulation Study Descriptionmentioning

confidence: 99%

Section: Simulation Study Descriptionmentioning

confidence: 99%

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

Xiong

Chen

et al. 2016

Transportation Research Part C: Emerging Technologies

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“…The state of the art of such models includes simulation-based approaches such as MITSIMLab [61,62], TransModeler [63], DynaMIT [19,20], DYNASMART [42], RENAISSANCE [59], DynusT [27], Dynameq [43] and VISTA [25]. The importance and complexity of model calibration has been recognized and documented.…”

Section: Archived Data (Multiple Days)mentioning

confidence: 99%

A Synthesis of emerging data collection technologies and their impact on traffic management applications

Antoniou

Balakrishna²,

Koutsopoulos

2011

Eur. Transp. Res. Rev.

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Purpose The objective of this research is to provide an overview of emerging data collection technologies and their impact on traffic management applications. Methods Several existing and emerging surveillance technologies are being used for traffic data collection. Each of these technologies has different technical characteristics and operating principles, which determine the types of data collected, accuracy of the measurements, levels of maturity, feasibility and cost, and network coverage. This paper reviews the different sources of traffic surveillance data currently employed, and the types of traffic management applications they may support. Results Automated Vehicle Identification data have several applications in traffic management and many more are certain to emerge as these data become more widely available, reliable, and accessible. Representative examples in this field are presented. Furthermore, the fusion of condition information with traffic data can result in better and more responsive dynamic traffic management applications with a richer data background. ConclusionsThe current state-of-the-art of traffic modeling is discussed, in the context of using emerging data sources for better planning, operations and dynamic management of road networks.

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“…During the iterative computational process, the time-dependent link travel time and intersection delays are input into the time-dependent shortest path algorithm. The vehicle-simulation mechanism follows anisotropic mesoscopic simulation logic in that individual vehicles are generated with individual attributes such as departure time, origin, destination, occupancy, vehicle type, and evacuation route (15,16). During the simulation, a vehicle's movement follows a speed-density relationship-a widely known correlation between speed and density that describes traffic flow-to ensure that the millions of simulated vehicles exhibit realistic traffic flow characteristics at the macroscopic level.…”

Section: Traffic Simulation and Assignment Modelmentioning

confidence: 99%

Approach to Modeling Demand and Supply for a Short-Notice Evacuation

Noh

Chiu

Zheng

et al. 2009

Transportation Research Record

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than 24 or 48 h, as in the case of a hurricane evacuation). When the time to evacuate is considerably less, people may need to be evacuated directly from their current locations. The timing and magnitude of certain types of disasters may require faster reactions. People may receive short notice about the emergency and their need to evacuate, providing little or no time to return home before evacuating. In addition, for some disasters, the spatial extent of the evacuated area may change over time. This problem may be exacerbated by congestion around the evacuated area. For example, to manage traffic flows effectively in the event of a short-notice disaster such as a wildfire or flash flood, it may be useful to know which areas must be evacuated first and the likely impact on roadway congestion throughout the region.Several studies have been conducted on estimating demand characteristics for an evacuation, particularly focusing on the factors of trip generation, trip distribution, and trip timing. Some research has focused on estimating a trip generation model, such as the logistic model and the neural network model, using hurricane survey data (1-5). These models estimate a probability of leaving an area as a function of time as the disaster progresses. Other models estimate evacuation demand using socioeconomic data (6, 7). Similar to these last two studies, the present study considers the case without existing evacuation survey data for estimating a model of trip generation; existing trip generation data from existing urban planning models are used; such is the common situation faced by most regional transportation and emergency management agencies. In addition, models used for trip distribution for evacuation include the gravity model, the intervening opportunity model, and the multinomial logit model. Finally, a type of logistic model has been used for vehicle loading, describing the time of departure (6-8).Methods of estimating demand for a short-notice evacuation have not been discussed at length in the existing literature. In this paper, an estimation process is proposed for a short-notice evacuation. The method uses "on-hand" data typically generated through existing travel demand models at many metropolitan planning organizations. In this context, the proposed trip generation and distribution models are developed using existing trip matrices based on existing, calibrated travel demand models. It generally assumes that no separate set of behavioral data is available for estimating evacuation demand. Trip matrices are suggested for use as the basis for generating trip distribution; this trip distribution approach was inspired by Southworth (7). Additionally, a time-dependent evacuee departure model is proposed that considers a multiple-zone evacuation strategy related to that of Tweedie et al. (8). Approach to Modeling Demand and Supply for a Short-Notice EvacuationHyunsoo Noh, Yi-Chang Chiu, Hong Zheng, Mark Hickman, and Pitu Mirchandani 91 As part of disaster mitigation and evacuation planning, planners ...

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Development and calibration of the Anisotropic Mesoscopic Simulation model for uninterrupted flow facilities

Cited by 63 publications

References 26 publications

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

Agent-based en-route diversion: Dynamic behavioral responses and network performance represented by Macroscopic Fundamental Diagrams

A Synthesis of emerging data collection technologies and their impact on traffic management applications

Approach to Modeling Demand and Supply for a Short-Notice Evacuation

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