Large-scale, high-ﬁdelity dynamic traﬃc assignment: framework and real-world case studies

Yang, Qifan; Balakrishna, Ramachandran; Morgan, Daniel; Slavin, Howard

doi:10.1016/j.trpro.2017.05.152

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Generally, the OD flows have to be estimated with some algorithms based on the historical information, and even the real-time surveillance data for online applications. The supply side loads the list of vehicles onto a mesoscopic or microscopic traffic simulator to simulate the complete movement trajectory of each vehicle from its origin to the destination along the given path, according to some traffic flow models, which basically differentiate various traffic simulation systems [14,15]. The trajectories of all vehicles in the network are merged, diverged, stacked or intertwined, some like ripples in a lake.…”

Section: Literature Reviewmentioning

confidence: 99%

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Lin

2023

Sustainability

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Intelligent Transportation Systems (ITS) have the potential to improve traffic conditions and reduce travel delays. As a decision support software system for ITS, DynasTIM is based on the principle of dynamic traffic assignment and developed for real-time online simulation, prediction and optimization of dynamic traffic flows in urban or expressway networks. This paper introduces the models, algorithms and some typical applications of DynasTIM. The main contents include: the functional architecture; the application architecture of the system; dynamic OD (Origin-Destination) flows estimation method with novel formula for assignment matrix computation; mesoscopic traffic model using variable-length speed influence region and calibrating speed online based on connected vehicles data; and parallel SPSA algorithm based urban area signal optimization method. The functions of DynasTIM are implemented basically through three main modules: state estimation (ES), state prediction and control strategy optimization (PS&CSO), and guidance strategy optimization (GSO). The case study is aimed at the populated Futian Central Business District (CBD) road network in Shenzhen, China, which has an area of about 7 square kilometers. Based on the archived turning counts collected from 359 video traffic detection locations, DynasTIM was calibrated offline for this network, in order to validate the capability of simulating actual traffic conditions, and to set up basic conditions for testing signal optimization methods. The results show that the simulation output flows of DynasTIM have fairly good matching accuracy with the real surveillance flows in the field. Furthermore, for the CBD network with 38 signalized intersections, the signal optimization method is evaluated and better signal timing plans are found which can reduce about 13% average travel delay, compared with the signal plans currently implemented in the field.

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Section: Literature Reviewmentioning

confidence: 99%

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Lin

2023

Sustainability

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“…Simulation analysis heavily relies on software as the primary tool for facilitating the calculation process [10]. The features of four different simulation programs: AIMSUN [11], TransModeler [12], CORSIM [13], and VISSIM were analyzed by Salgado et al [14] and Hadi et al [15]. Although each software package has advantages, the study ultimately chose VISSIM due to its superior vehicle routing capabilities, total output, stability, and extensive supporting documents accompanied by animations [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Assuming the availability of assignment matrices, the calibration procedure can prioritize the adjustment of other parameters and variables to enhance the precision and dependability of the comprehensive model [11]. Yang and Slavin [12] took a different tack by extending the origin-destination estimation process to incorporate a route choice model, but they did so assuming that the model parameters are immutable. The model's parameters are fine-tuned during calibration by comparing the simulated and observed traffic flows.…”

Section: Introductionmentioning

confidence: 99%

Calibrating and Validation Microscopic Traffic Simulation Models VISSIM for Enhanced Highway Capacity Planning

Hafram

Valery

Hasim

2023

IJE

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This research aims to calibrate and validate the VISSIM simulation model tool by comparing field data with simulation data. The ultimate goal is to evaluate traffic performance by comparing simulation results with direct observations in the field. This study uses modeling to determine a road segment's maximum flow volume. This study was conducted in Makassar, South Sulawesi, Indonesia, on Jalan Veteran Selatan. The method uses two main inputs: urban road primary capacity data from the Indonesian Highway Capacity Manual (IHCM 1997) and roadside activity data from PTV VISSIM. The GEH and MAPE have commonly used metrics for measuring the accuracy of simulation models and calibration measurements using driving behavior parameters. The research results obtained for validation measurements have met the requirements. Namely, the obtained MEPE value (7.38%) is 10% smaller than the obtained GEH value (2.032 and 3.961), which is still more than 5.00. The calibration measurements obtained the suitability of the vehicle location and intervehicle spacing in the simulation model (VISSIM) with the actual field conditions. The results obtained from using VISSIM can be reliable and helpful in designing and optimizing urban transportation systems in the future. It is essential to remember that traffic simulation with VISSIM is only a transportation decision-making and planning tool and must be combined with field observations and accurate data for adequate and efficient transportation solutions.

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“…Simulation-based dynamic traffic assignment (DTA) is an effective tool for analyzing transportation systems for both operational and planning purposes (Schreiter, Wageningen-Kessels, Yuan, Van Lint, & Hoogendoorn, 2012). The aim of simulation-based DTA is to calculate the dynamic path flow distribution for all the origin-destination (OD) pairs in the traffic network depending on one equilibrium rule (Yang, Balakrishna, Morgan, & Slavin, 2017). The well-known equilibrium rule is user equilibrium (UE), wherein all users experience minimum travel time (Wardrop, 1952).…”

Section: Introductionmentioning

confidence: 99%

Simulation‐based dynamic traffic assignment: Meta‐heuristic solution methods with parallel computing

Ameli

Lebacque

Leclercq

2020

Computer aided Civil Eng

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The aim of this study is to solve the large‐scale dynamic traffic assignment (DTA) model using a simulation‐based framework, which is computationally a challenging problem. Many studies have been performed on developing an efficient algorithm to solve DTA. Most of the existing algorithms are based on path‐swapping descent direction methods. From the computational standpoint, the main drawback of these methods is that they cannot be parallelized. This is because the existing algorithms need to know the results of the last iteration to determine the next best path flow for the next iteration. Thus, their performance depends on the single initial or intermediate solution, which means they exploit a solution that satisfies the equilibrium conditions more than explore the solution space for the optimal solution. More specifically, the goal of this study is to overcome the drawbacks of serial algorithms by using meta‐heuristic algorithms known to be parallelizable and that have never been applied to the simulation‐based DTA problem. This study proposes two new solution methods: a new extension of the simulated annealing and an adapted genetic algorithm. With parallel simulation, the algorithm runs more simulations in comparison with existing methods, but the algorithm explores the solution space better and therefore obtains better solutions in terms of closeness to the optimal solution and computation time compared to classical methods.

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Large-scale, high-ﬁdelity dynamic traﬃc assignment: framework and real-world case studies

Cited by 6 publications

References 6 publications

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System

Calibrating and Validation Microscopic Traffic Simulation Models VISSIM for Enhanced Highway Capacity Planning

Simulation‐based dynamic traffic assignment: Meta‐heuristic solution methods with parallel computing

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