Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari, Haitham; Koting, Suhana; Ahmed, Ali Najah; El-shafie, Ahmed

doi:10.1016/j.heliyon.2024.e25936

Cited by 3 publications

(1 citation statement)

References 160 publications

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“…By generating results for a given urban network, suburban area, or residential neighborhood and for known initial state of charge (SOC) level of vehicles, it is possible to estimate where the greatest need for vehicle charging is. Such energy consumption results can be generated using test drives or by simulating specific traffic conditions in simulation software [53,54]. These issues will be the subject of the further development of this work.…”

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confidence: 99%

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Mądziel

2024

Energies

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This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks.

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confidence: 99%

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Mądziel

2024

Energies

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Calibration method for microscopic traffic simulation considering lane difference

Liu,

Deng,

et al. 2024

SIMULATION

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Lane-level differences in traffic conditions on urban roads are becoming increasingly significant. To remedy this problem, this study proposes a method for the microscopic traffic simulation calibration problem that considers the complexity of traffic conditions on-road sections and the differences in operating states between lanes. A simulation model was established by collecting actual data. Calibration parameters were determined using sensitivity analysis. A calibration model was built to minimize the relative errors of the roadway efficiency and lane differential indicators. The values of these parameters were obtained using a genetic algorithm (GA). The calibration processes were automated using programming. To assess the reliability of the proposed method, we conducted five sets of comparative experiments focusing on two aspects: calibration methods and algorithm utilization. Results indicate that the proposed method significantly enhances simulation accuracy, particularly in lane-level traffic simulations. In comparison to approaches considering only section-level traffic characteristics and default application software parameters, the proposed method yielded reductions in errors by 3.7%, 5.8%, 6.6%, and 3.2% for simulating lane occupancy rate and cross-section flow. The proposed method demonstrated a simulation error of approximately 5%, while the artificial neural network method was about 7%, validating the effectiveness of the algorithms employed. It can play a crucial role in multilane traffic flow, intelligent driving tests, vehicle–road cooperation, and other related study areas.

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Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analysis

Mądziel

2024

Preprint

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The paper outlines a methodology for developing a model to estimate energy consumption in electric vehicles (EVs). The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. Such models hold particular significance for microscale road analyses. The resultant model for test data in summer conditions demonstrates validation indicators with an R2 of 86% and an MSE of 1.4, while for winter conditions, the values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision-makers in city road management and infrastructure planners, aiding both cognitive understanding and better planning of charging infrastructure networks.

show abstract

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Cited by 3 publications

References 160 publications

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

Calibration method for microscopic traffic simulation considering lane difference

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analysis

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