Retrospect and prospect for subarea travel demand modeling: an empirical study

Abstract

At present, customized subarea models have been widely used in local transportation planning throughout the USA. A subarea model's biggest strengths lie in its more detailed and accurate modeling outputs which better meet local planning requirements. In addition, a subarea model can substantially reduce database size and model running time. In spite of these advantages, subarea models remain quite weak in modeling transit projects, smart growth measures, air quality conformity, and other areas. In addition to evaluating subarea models, this paper uses the Irvine Transportation Analysis Model (ITAM) as an empirical case of subarea model to illustrate the remedial procedures in maintaining its consistency with the regional model of the Orange County Transportation Analysis Model (OCTAM). Looking into the future, subarea models face both opportunities and challenges. More GIS applications, travel surveys, micro-simulation software utilization, and modeling improvements are expected to be incorporated into the subarea modeling process.     

基于时空特征的干线协调控制子区划分方法

为应对干线交通流的时变性和空间分布差异性给干线协调控制带来的影响,本文提出了基于交通流时空特征的干线协调控制子区划分方法。首先利用交通流时间相关系数和空间相关系数分析交通流的时空特征,根据交通流的时间特征改进时段划分方法;引入相邻交叉口关联系数和不停车通过路口车辆数作为子区划分指标,在时段划分的结果上进行子区划分,以交通流的空间特征改进合并指数法,实现子区划分方案的更新。最后,以玉林市民主路为例验证该方法,结果显示,干线系统的总延误、车均停车延误时间、车均停车次数分别降低了19.5%、23.4%、16.7%,该方法比传统交通控制时段划分方法更能适应交通流的时空变化趋势,显著提高干线交通流的运行效率。     

基于MFD的城市区域路网多子区协调控制策略

针对城市路网中区域性的大范围交通拥堵问题,提出了基于宏观基本图(Macroscopic Fundamental Diagram,MFD)的多子区协调控制策略,以提升路网的整体运行效益.该策略将城市区域路网划分为多个子区,每个子区的交通流又划分为内部流和转移流,综合两者建立了基于MFD的多子区交通流模型,并给出了对各子区交通流诱导时的边界约束条件;通过调节子区边界控制输入,设计了边界反馈控制器对各子区转移流进行动态诱导,继而进行了迭代分析,以判断其是否满足边界约束,并对控制器进行了Lyapunov稳定性分析.仿真结果表明,所提策略使城市区域路网中各子区车辆总数渐近收敛于设定值,且整体平均流量提高了约11%,大范围交通拥堵状况得到明显缓解.     

A robust method for estimating transit passenger trajectories using automated data

Development of an origin-destination demand matrix is crucial for transit planning. The development process is facilitated by automated transit smart card data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specification (GTFS) data to infer the most likely trajectory of individual transit passengers. The method relaxes the assumptions on various parameters used in the existing trip chaining algorithms such as transfer walking distance threshold, buffer distance for selecting the boarding location, time window for selecting the vehicle trip, etc. The method also resolves issues related to errors in GPS location recorded by AFC systems or selection of incorrect sub-route from GTFS data. The proposed trip chaining method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). Based on the consecutive tags of the passenger, the proposed algorithm is also modified for pay-exit cases. The method is compared to previous methods developed by the researchers and shows improvement in the number of inferred cases. Finally, results are visualized to understand the route ridership and geographical pattern of trips.     

Origin-destination trip table estimation based on subarea network OD flow and vehicle trajectory data

Identifying accurate origin-destination (O-D) travel demand is one of the most important and challenging tasks in the transportation planning field. Recently, a wide range of traffic data has been made available. This paper proposes an O-D estimation model using multiple field data. This study takes advantage of emerging technologies – car navigation systems, highway toll collecting systems and link traffic counts – to determine O-D demand. The proposed method is unique since these multiple data are combined to improve the accuracy of O-D estimation for an entire network. We tested our model on a sample network and found great potential for using multiple data as a means of O-D estimation. The errors of a single input data source do not critically affect the model’s overall accuracy, meaning that combining multiple data provides resilience to these errors. It is suggested that the model is a feasible means for more reliable O-D estimation.     

基于VISUM仿真软件的O-D矩阵反推技术

提出了基于VISUM仿真软件的O-D矩阵反推技术,就该技术的核心原理与实施应用给予详细的阐述。该技术运用动态多路径的平衡分配法进行交通分配,充分利用了交通、土地利用和道路网现状调查所获取的各种信息,并以VISUM宏观交通仿真软件为技术基础。     

基于UTC-SCOOT的特殊相邻路口交通信号控制

根据相邻路口的交通特性,基于UTC-SCOOT提出控制这种特殊路口的方法。通过实例证明,该方案可在一定程度上改善相邻路口的交通状态。