互联车辆环境下行程时间预测的工作区仿真模型
A Work Zone Simulation Model for Travel Time Prediction in a Connected Vehicle Environment
摘要：
道路网络中的工作区瓶颈可能导致交通延误、排放和安全问题。工作区旅行时间的准确测量和预测可以帮助旅行者做出更好的路线决策，从而减轻其影响。从历史上看，用于旅行时间分析的数据来自固定回路探测器，其安装和维护成本很高。借助车联网技术（如车对基础设施），便携式路侧单元（RSU）可以位于工作区段内和周围，以与车辆通信并收集交通数据。建立了一个PARAMICS仿真模型，用于连接车辆环境中的原型高速公路工作区，使用纽约州104号公路的交通需求数据来测试这一想法。对于模拟，沿工作区段放置12个RSU，并从模拟结果中提取16个变量，以探索行程时间估计和预测。在行程时间分析中，建立了线性回归、多元自适应回归样条（MARS）、逐步回归和弹性网络四种模型。结果表明，所考虑的建模方法在均方误差根（RMSE）方面具有相似的性能，这为基于其他因素（包括根据各种模型中确定的重要变量的RSU的数量和位置）进行模型选择提供了机会。在这四种方法中，逐步回归模型只需要两个RSU的变量：一个放置在工作区的上游，一个在工作区的末端。
英文摘要：
A work zone bottleneck in a roadway network can cause traffic delays, emissions and safety issues. Accurate measurement and prediction of work zone travel time can help travelers make better routing decisions and therefore mitigate its impact. Historically, data used for travel time analyses comes from fixed loop detectors, which are expensive to install and maintain. With connected vehicle technology, such as Vehicle-to-Infrastructure, portable roadside unit (RSU) can be located in and around a work zone segment to communicate with the vehicles and collect traffic data. A PARAMICS simulation model for a prototypical freeway work zone in a connected vehicle environment was built to test this idea using traffic demand data from NY State Route 104. For the simulation, twelve RSUs were placed along the work zone segment and sixteen variables were extracted from the simulation results to explore travel time estimation and prediction. For the travel time analysis, four types of models were constructed, including linear regression, multivariate adaptive regression splines (MARS), stepwise regression and elastic net. The results show that the modeling approaches under consideration have similar performance in terms of the Root of Mean Square Error (RMSE), which provides an opportunity for model selection based on additional factors including the number and locations of the RSUs according to the significant variables identified in the various models. Among the four approaches, the stepwise regression model only needs variables from two RSUs: one placed sufficiently upstream of the work zone and one at the end of the work zone.
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