Applications of Artificial Intelligence Paradigms to Decision Support in Real-Time Traffic Management

Abstract

Decision support for real-time traffic management is a critical component for the success of intelligent transportation systems. Theoretically, microscopic simulation models can be used to evaluate traffic management strategies in real time before a course of action is recommended. However, the problem is that the strategies would have to be evaluated in real time; this might not be computationally feasible for large-scale networks and complex simulation models. To address this problem, two artificial intelligence (AI) paradigms—support vector regression (SVR) and case-based reasoning (CBR)—are presented as alternatives to the simulation models as a decision support tool. Specifically, prototype SVR and CBR decision support tools are developed and used to evaluate the likely impacts of implementing diversion strategies in response to incidents on a highway network in Anderson, South Carolina. The performances of the two prototypes are then evaluated by a comparison of their predictions of traffic conditions with those obtained from VISSIM, a microscopic simulation model. Although the prototype systems’ predictions were comparable to those obtained by simulation, their run times were only fractions of the time required by the simulation model. Moreover, SVR performance is superior to that of CBR for most cases considered. The study results provide motivation for consideration of the proposed AI paradigms as potential decision support tools for real-time transportation management applications.

The Impact of Traffic Incident Locations on a Metropolitan Evacuation

“…Findings of this research could be augmented with other decision support models that support realtime traffic management including traffic diversion during an evacuation [38,39].…”

Optimal road accident case retrieval algorithm based on k-nearest neighbor

“…The system randomly generates 40 experimental cases and each one is retrieved in the original accident case base and case retrieval base, respectively, based on the similarity models (equations (4)-(6)) of road accident cases. The parameters of case feature weights are set according to the values depicted in Table 1 and the matching degree of retrieved case set can be calculated in equations (8)- (10). The results are shown in Figures 3 and 4.…”

“…Sadek et al 9 used NN approach to retrieve traffic diversion strategies with the weight value of each case feature assumed to be equal to 1.0. Chowdhury et al 10 analyzed the diversion strategies under incident conditions based on NN algorithm in CBR model. The performance of case retrieval was evaluated with subjectively inputting eight different combinations of weights to case features (e.g.…”

“…volume multiplier, incident duration, percentage of drivers diverting, and incident location) and the perfect combination of weights was selected. 10 Hoogendoorn et al 11 developed a CBR system to help make traffic management decisions, using mean fuzzy membership to determine the case weights value. Ji and Liu 12 also proposed NN algorithm to retrieve similar decisionmaking cases for traffic congestion management with analytic hierarchy process (AHP) approach to determine the weight of each case feature.…”

The Bus Arrival Time Service Based on Dynamic Traffic Information

“…提出使用卡尔曼滤波模型来跟踪车辆的位 置，并在 Seattle 的华盛顿地区结合自动车辆定位系统 (AVL)和历史数据来预测公交车辆的到站时间，但是 他们并没有将停靠时间作为模型的独立变量。 Chenro [3] 建立的模型假设城市的交通状况是周期进行变化的， 且在特定路段内历史行程时间与当前行程时间的比 值是不变的。在历史数据库的基础之上，建立预测模 型，利用实时定位数据调整公交车的预测到站时间。 Jeong 和 Rilett [4] 基于德克萨斯州休斯顿市采集的公交 车实时 AVL(车辆自动定位系统)数据，在给定交通拥 挤实时信息和公交车辆在每个站点的停靠时间的条 件下，建立了基于历史统计的模型、多变量回归模型 和人工神经网络模型，通过比较三种模型的平均绝对 百分误差发现人工神经网络模型 [5] 的预测精度要比历 史数据模型和回归模型高 [6] 。 Shalaby 和 Farhan [7] 提出了基于卡尔曼滤波方法 的公交车行程时问预测模型，他们使用从多伦多城区 采集而来的车辆定位数据来进行分析预测，发现卡尔 曼滤波方法比历史数据模型，回归模型以及神经网络 模型效果更好。他们采集了 2001 年 5 月每个周中 5 天的数据，选择其中 4 天的数据来训练模型，剩余一 天的数据用来做测试。此外，他们还提出了一个分开 的卡尔曼滤波预测算法 [8] ，用来计算运行时间和停靠 时间。所建立的历史平均模型，回归模型以及神经网 络模型都包括了路段行程时间中的停靠时间，也就是 说模型对于停靠时间和运行时间没有分开来考虑。他 们将路段定义为在两个检查时间点站点间的距离，每 个路段包括 8 个公交站点，因此他们只预测在时问检 查点的停靠时间。 温惠英等 [9] "利用灰色理论对影响行程时间序列 的各因素进行灰色关联分析，根据灰色关联度 [10]…”