Exploring the Opportunities of Using an Innovative Source of Origin-Destination Data in Regional Transportation Models (original) (raw)
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Using Real Traffic Data for ITS Simulation: Procedure and Validation
The 13th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2016)
The high levels of urban traffic is becoming a main concern in our societies, generating problems such as excessive fuel consumption and CO 2 emissions. Recently, Intelligent Transportation Systems (ITS) have emerged as a way to mitigate these problems. However, traffic analysis and improvement typically rely on simulations, which should be as realistic as possible. Meeting this requirement can be complex when the actual traffic patterns, defined through an origin/destination (O-D) matrix for the vehicles in a city, remain unknown. In this paper we propose a novel technique in order to import traffic data to the SUMO mobility simulation tool. In particular, our approach starts from induction loop measurements available from traffic authorities, and then it uses the DFROUTER tool, along with a heuristic, to generate an O-D matrix for traffic that resembles the real traffic distribution. We apply our technique to the city of Valencia, and the obtained results are then compared through simulation to other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), demonstrating the validity of our approach.
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For over a decade, the San Francisco County Transportation Authority (the Authority) has used an activity-based travel model, known as SF-CHAMP, for project evaluation. While SF-CHAMP has generally served the agency’s needs well, the lack of realism in the static assignment model it uses has been limiting for certain applications. Therefore, the Authority sought to develop a citywide dynamic traffic assignment (DTA) model. At a project-level, planners have found DTA to be a useful post-process to static traffic assignment to (1) understand the effect of projects that can be measured at the mesoscopic level, and (2) serve as a tidier transition from static traffic assignment into a traffic microsimulation model. At a higher level, planners are interested in incorporating DTA’s ability to more accurately evaluate reliability, transit/auto interactions, and operational treatments into an integrated modeling framework that is capable of evaluating changes in travel behavior (an integrat...
Transportation Research Record: Journal of the Transportation Research Board, 2018
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How accurate is the regional travel demand model in mimicking real-world travel times?
Urban, Planning and Transport Research, 2019
This paper provides insights pertaining to the validity of a regional travel demand model in mimicking real-world travel times. The estimated travel times from the regional travel demand model, for the base year 2015, for Mecklenburg County in the city of Charlotte, North Carolina (NC) were compared with travel time statistics from a private data source, for the same year. The results indicate that the estimated travel times from the regional travel demand model are typically lower than the 85 th percentile travel times, irrespective of the link speed limit. The estimated travel times for the Central Business District (CBD) area type are moderately correlated with the travel time statistics from the private data source, irrespective of the time of the day. For all the other area types, stronger correlations were observed when the estimated travel times from the regional travel demand model are compared with 10 th to 50 th percentile travel times. The calculated Pearson correlation coefficients are low for morning and evening peak periods compared to midday and nighttime period, indicating the inability of the regional travel demand model in mimicking congested traffic conditions accurately.
Calibration of the Demand Simulator in a Dynamic Traffic Assignment System
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The Art of the Utilization of Traffic Simulation Models: How Do We Make Them Reliable Tools
This paper, firstly, describes the current status of the utilization of traffic simulation models. Evaluation for the model’s application in Japan was based on a questionnaire survey. Secondly, the Best Practice Manual for Simulation Application, which is currently being developed, is discussed. In addition some exerts from the manual in regards to addressing the issues of simulation application are presented: i.e. i) understanding the models’ nature through verification and validation; ii) OD estimation from vehicle counts; iii) model parameter calibration; and iv) indices to measure the reproducibility. Finally this paper introduces the Clearing House of Traffic Simulation Models. These models promote simulation utilization.