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Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections

机译:识别VISSIM仿真模型和SSAM是否为信号交叉口的实测交通冲突提供合理的估计

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The primary objective of this study was to identify if the VISSIM simulation model and the Surrogate Safety Assessment Model (SSAM) approach provided reasonable estimates for the traffic conflicts measured at signalized intersections. A total of 80 h of traffic data and traffic conflicts data were collected at ten signalized intersections. Simulated conflicts generated by the VISSIM simulation model and identified by SSAM were compared to the traffic conflicts measured in the field. Of particular interest was to identify if the consistency between the simulated and the observed conflicts could be improved by calibrating VISSIM simulation models and adjusting threshold values used for defining simulated conflicts in SSAM. A two-stage procedure was proposed in this study to calibrate and validate the VISSIM simulation models. It was found that the two-stage calibration procedure improved the goodness-of-fit between the simulated conflicts and the real-world conflicts. Linear regression models were developed to study the relationship between the simulated conflicts and the observed conflicts. Results of data analysis showed that there was a reasonable goodness-of-fit between the simulated and the observed rear-end and total conflicts. However, it was also found that the simulated conflicts were not good indicators for the traffic conflicts generated by unexpected driving maneuvers such as illegal lane-changes in the real world. The research team further tested the prediction performance of the conflict prediction models using the simulated conflicts as independent variables. It was found that the conflict prediction models provided acceptable prediction performance for the total and the rear-end conflicts with a MAPE value of 18% and 20%, respectively. However, the prediction performance of the conflict prediction models for the crossing and the lane change conflicts was only moderate with a MAPE value of 31% and 38%, respectively.
机译:这项研究的主要目的是确定VISSIM仿真模型和替代安全评估模型(SSAM)方法是否为信号交叉口处的交通冲突提供了合理的估计。在十个信号交叉口收集了总计80小时的交通数据和交通冲突数据。将由VISSIM仿真模型生成并由SSAM识别的模拟冲突与现场测量的交通冲突进行了比较。特别令人感兴趣的是,确定是否可以通过校准VISSIM仿真模型并调整用于定义SSAM中模拟冲突的阈值来改善模拟冲突和观察到的冲突之间的一致性。在这项研究中提出了一个两阶段的程序来校准和验证VISSIM仿真模型。发现两阶段校准程序改善了模拟冲突与现实冲突之间的拟合优度。开发了线性回归模型以研究模拟冲突与观察到的冲突之间的关系。数据分析结果表明,模拟冲突与观察到的后端冲突和总冲突之间存在合理的拟合优度。但是,还发现,模拟冲突不是由意外驾驶行为(例如,现实世界中的非法换道)所产生的交通冲突的良好指标。研究团队使用模拟的冲突作为自变量,进一步测试了冲突预测模型的预测性能。发现冲突预测模型为总冲突和后端冲突提供了可接受的预测性能,MAPE值分别为18%和20%。但是,冲突预测模型对交叉路口和车道变更冲突的预测性能仅为中等,MAPE值分别为31%和38%。

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