Optimization of robust area traffic control with equilibrium flow under demand uncertainty

For area traffic control road network under realization of uncertain travel demand, a robust signal setting is investigated in this paper. Due to certain hierarchy in a decision-making order, a min–max bilevel program is proposed. A new solution method is presented to determine a Nash–Stackelberg solution where a proposed signal setting is found for area traffic control under demand uncertainty. In order to investigate the robustness of the proposed signal settings, numerical computations are performed for various initial data sets in a medium-sized example road network. Good computational results indicated that the proposed signal settings can successfully reduce a worst-case travel cost substantially while incurring a relatively slight loss of optimality with respect to the optimal deterministic solutions for nominal travel demands. Particularly, our computation results showed that the proposed signal settings become even attractive as demand growth increases under a worst-case realization taken by uncertain travel demands.     

The time-dependent prize-collecting arc routing problem

A new problem is introduced named the Time-Dependent Prize-Collecting Arc Routing Problem (TD-PARP). It is particularly relevant to situations where a transport manager has to choose between a number of full truck load pick-ups and deliveries on a road network where travel times change with the time of day. Two metaheuristic algorithms, one based on Variable Neighborhood Search and one based on Tabu Search, are proposed and tested for a set of benchmark problems, generated from real road networks and travel time information. Both algorithms are capable of finding good solutions, though the VNS approach generally shows better performance.     

Route planning model of multi-agent system for a supply chain management

Sometimes in travel planning, finding the best route to the road transportation network by considering the environmental conditions that are affecting the actual time travel of the travellers are vital especially in handling the logistic operations in supply chain management (SCM). Furthermore, the policy strategy is needed in order to influence the managers or drivers to find the optimum and the most effective route for a trip plan in supporting the logistic operations of SCM. In this paper we analyze the effectiveness of the coordination model of the environmental conditions that are affecting for the travelling time based on multi-agent system for a road transportation network for supply chain management. A number of experimental cases have been used to evaluate the proposed approach transportation network problems in some Malaysian cities. Finally, experimental results affirmed that the proposed approach is practical and efficient.     

Finding Reliable Shortest Paths in Road Networks Under Uncertainty

The aim of this study is to investigate the solution algorithm for solving the problem of determining reliable shortest paths in road networks with stochastic travel times. The availability of reliable shortest paths enables travelers, in the face of travel time uncertainty, to plan their trips with a pre-specified on-time arrival probability. In this study, the reliable shortest path between origin and destination nodes is determined using a multiple-criteria shortest path approach when link travel times follow normal distributions. The dominance conditions involved in such problems are established, thereby reducing the number of generated non-dominated paths during the search processes. Two solution algorithms, multi-criteria label-setting and A* algorithms, are proposed and their complexities analyzed. Computational results using large scale networks are presented. Numerical examples using data from a real-world advanced traveller information system is also given to illustrate the applicability of the solution algorithms in practice.     

Optimization of a nonlinear area traffic control system with elastic demand

An area traffic control network system is considered in this paper. Optimal signal settings can be determined while trip rates and network flow are in equilibrium. This problem can be formulated as a nonlinear mathematical program with equilibrium constraints. For the objective function, the system performance can be defined as a function of signal setting variables. For the constraint set, a user equilibrium traffic assignment with elastic demand obeying Wardrop’s first principle is formulated as a variational inequality problem. Due to the nonlinearity and non-differentiability of the perturbed solutions in equilibrium constraints, a non-smooth approach is investigated in this paper. Numerical tests are performed using a variety of example road networks to quantify the effectiveness and robustness of the proposed method.     

Type-2 fuzzy logic based urban traffic management

This paper presents a multi-agent system based on type-2 fuzzy decision module for traffic signal control in a complex urban road network. The distributed agent architecture using type-2 fuzzy set based controller was designed for optimizing green time in a traffic signal to reduce the total delay experienced by vehicles. A section of the Central Business District of Singapore simulated using PARAMICS software was used as a test bed for validating the proposed agent architecture for the signal control. The performance of the proposed multi-agent controller was compared with a hybrid neural network based hierarchical multi-agent system (HMS) controller and real-time adaptive traffic controller (GLIDE) currently used in Singapore. The performance metrics used for evaluation were total mean delay experienced by the vehicles to travel from source to destination and the current mean speed of vehicles inside the road network. The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.     

基于信号控制的城市路网旅行时间计算模型

交通控制信号对交通流的影响是干扰实时交通数据计算准确性的重要因素。为此,提出一种基于信号控制的城市路网旅行时间计算模型。将城市道路的旅行时间分为2个部分,即路链有效旅行时间和路口延误时间,设计改进的信号控制延误模型用于计算路口延误时长,并给出路链合并算法。实验结果表明,该模型起点到终点的旅行时间误差率能降低5%~15%。     

考虑交通拥堵规避的低碳时变车辆路径问题研究

针对时变路网条件下的低碳车辆路径问题,首先,分析车辆离散行驶速度与连续行驶时间之间的关系,依据“先进先出”准则设计基于时间段划分的路段行驶时间计算方法,引入考虑车辆速度、实时载重、行驶距离与道路坡度因素的碳排放计算函数;然后,在此基础上以所有车辆的碳排放量最小为目标构建低碳时变车辆路径问题数学模型;最后,引入交通拥堵指数,设计交通拥堵规避方法,并根据模型特点设计一种改进蚁群算法求解.实验结果表明,所提出方法能有效规避交通拥堵、缩短车辆行驶时间、减少车辆碳排放,促进物流配送与生态环境和谐发展.     

面向车联网的交通流参数检测

将RFID技术应用到交通流检测中是交通检测的新趋势。基于RFID的车联网进行讨论,分析及解决其设计参数选取、系统结构设计和邻道干扰等问题。对如何检测交通流的空间密集度（Density）、交通量（Traffic volume）、空间平均速度（Space mean speed）、选择概率（Selection Probability）、道路通行时间（Road Travel Time）和道路延误时间（Road Delay Time）等参数进行分析,指出因引入RFID技术,使得以往不可准确检测的道路通行时间和道路延误时间及新提出的选择概率成为可测参数,并给出具体计算方式。通过仿真实验验证了检测方式的正确性和可行性,为进一步研究车联网提供了充分的理论基础。     

Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

Road boundary detection is essential for autonomous vehicle localization and decision-making, especially under GPS signal loss and lane discontinuities. For road boundary detection in structural environments, obstacle occlusions and large road curvature are two significant challenges. However, an effective and fast solution for these problems has remained elusive. To solve these problems, a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed. The proposed method consists of three main stages: 1) a multi-feature based method is applied to extract feature points; 2) a road-segmentation-line-based method is proposed for classifying left and right feature points; 3) an iterative Gaussian Process Regression (GPR) is employed for filtering out false points and extracting boundary points. To demonstrate the effectiveness of the proposed method, KITTI datasets is used for comprehensive experiments, and the performance of our approach is tested under different road conditions. Comprehensive experiments show the road-segmentation-line-based method can classify left, and right feature points on structured curved roads, and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions. Meanwhile, the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.      

基于ACP方法的平行手机信令数据分析系统

随着交通拥堵和公共安全问题的日趋严重，传统方案在道路监测和区域监测方面不仅成本高，准确性和可靠性也无法保证，因此无法给用户提供一整套综合全面的出行路线规划及旅游目的地选择等方面的相关指导.本文提出基于ACP方法的平行手机信令数据分析系统，将解决上述问题.本文主要基于ACP方法，包括人工社会、计算实验和平行执行，构建基于手机信令的人工监控场景和实际监控场景.实际监控场景和人工监控场景平行执行，人工监控场景用来模拟和实验复杂的实际监控场景，通过大量计算实验，进行各种模型的训练与评估，通过平行执行不断地更新和优化，实时指导实际监控场景；同时实际监控场景将结果反馈给人工监控场景，对人工监控场景模型进行修正.通过实际监控场景和人工监控场景之间的不断优化，可有效提高手机信令系统的实时性、准确性和可靠性，并最终满足不断增长的实时用户需求，保证用户出行的舒适性及安全性.     

A dynamic programming approach for finding shortest chains in a fuzzy network

Graph theory has numerous applications to problems in systems analysis, operations research, transportation, and economics. In many cases, however, some aspects of a graph-theoretic problem may be uncertain. For example, the vehicle travel time or vehicle capacity on a road network may not be known exactly. In such cases, it is natural to make use of fuzzy set theory to deal with the uncertainty. Here, we are concerned with finding shortest chains in a graph with fuzzy distance for every edge. We propose a dynamic programming approach to solve the fuzzy shortest chain problem using a suitable ranking method. By using MATLAB, two illustrative examples are worked out to demonstrate the proposed algorithm.     

A comprehensive modeling framework for hazmat network design,hazmat response team location,and equity of risk

This paper considers a bi-level hazmat transportation network design problem in which hazmat shipments have to be transported over a road network between specified origin-destination points. The bi-level framework involves a regulatory authority and hazmat carriers. The control variables for the regulatory authority are locations of hazmat response teams and which additional links to include for hazmat travel. The regulatory authority (upper level) aims to minimize the maximum transport risk incurred by a transportation zone, which is related to risk equity. Our measure of risk incorporates the average response time to the hazmat incidents. Hazmat carriers (lower level) seek to minimize their travel cost. Using optimality conditions, we reformulate the non-linear bi-level model as a single-level mixed integer linear program, which is computationally tractable for medium size problems using a commercial solver. For large size problems, we propose a greedy heuristic approach, which we empirically demonstrate to find good solutions with reasonable computational effort. We also seek a robust solution to capture stochastic characteristics of the model. Experimental results are based on popular test networks from the Sioux Falls and Albany areas.     

出租车数据的城市道路网路段通行时间估计方法

城市路段通行时间估计能够更好地运营和管理城市交通。针对包含起点-终点位置,行程时间和距离信息的GPS行程数据,提出了一种城市道路网短时通行时间的估计模型。首先将城市道路网按照交叉路口分解为多个路段,并基于k-最短路径搜索方法分析司机行进路线。然后针对每一个路段,提出了双车道通行时间多项式关联关系模型,既能提升道路网通行时间精细度,又能避免因训练数据不足导致的路网通行时间过拟合问题。最后以最小化行程期望时间和实际行程时间之间的均方误差为优化目标,拟合道路网通行时间。在纽约出租车数据集上的实验结果表明,所提模型及方法相对于传统单车道估计方法能够更准确地估计城市道路网路段的通行时间。     

Exclusive Bus Lane Network Design: A Perspective from Intersection Operational Dynamics

Exclusive bus lanes have demonstrated their effectiveness in increasing capacity and efficiency. However, when discussing the optimal design of exclusive bus lanes at the network level, existing studies predominantly focus on the interaction between transit and private vehicles at the links, while operational dynamics at the intersections and their impacts were insufficiently addressed. This paper contributes to developing a mathematical model for exclusive bus lane network design with operational dynamics at intersections explicitly captured. In the proposed model, lane re-configuration for transit and private vehicles at both road segments and intersections along with signal timing optimization are integrated in a unified framework. The model features a bi-level structure with the objective for reducing the total system person travel time. A genetic algorithm embedded with Emme is used for yielding solutions to the model. Results from both numerical and empirical tests demonstrate the effectiveness of the proposed model and reveal that setting of exclusive bus lanes in the network without explicit consideration of intersection operational dynamics can significantly undermine the network performance with respect to both bus passengers and private vehicles.

     

Estimating congestion zones and travel time indexes based on the floating car data

Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones on a large road network, and (ii) estimation of travel times within congestion zones in the form of the time-varying Travel Time Indexes (TTIs). On the micro level, the traffic conditions, in the form of speed profiles were mapped to links in the road network. On the macro level, the observed area was divided into a fine-grained grid and represented as an image where each pixel indicated congestion intensity. Spatio-temporal characteristics of congestion zones were determined by morphological closing operation and Monte Carlo simulation coupled with temporal clustering. As a case study, the road network in Croatia was selected with spatio-temporal analysis differentiating between the summer season and the rest of the year season. To validate the proposed approach, three comparisons were conducted: (i) comparison to real routes' travel times driven in a controlled manner, (ii) comparison to historical trajectory dataset, and (iii) comparison to the state-of-the-art method. Compared to the real measured travel times, using zone's time-varying TTIs for travel time estimation resulted in the mean relative percentage error of 4.13%, with a minor difference to travel times estimated on the micro level, and a significant improvement compared to the current Croatian industrial navigation. The results support the feasibility of estimating congestion zones and time-varying TTIs on a large road network from FCD, with the application in urban planning and time-dependent routing operations due to: significant reduction in the data volume without notable quality loss, and meaningful reduction in the pre-processing computation time.     

Urban arterial traffic two-direction green wave intelligent coordination control technique and its application

This paper presents a new two-direction green wave intelligent control strategy to solve the coordination control problem of urban arterial traffic. The whole control structure includes two layers — the coordination layer and the control layer. Public cycle time, splits, inbound offset and outbound offset are calculated in the coordination layer. Public cycle time is adjusted by fuzzy neural networks (FNN) according to the traffic flow saturation degree of the key intersection. Splits are calculated based on historical and real-time traffic information. Offsets are calculated by the real-time average speeds. The control layer determines phase composition and adjusts splits at the end of each cycle. The target of this control strategy is to maximize the possibility for vehicles in each direction along the arterial road to pass the local intersection without stop while the utility efficiency of the green signal time is at relatively high level. The actual application results show the proposed method can decrease the average travel time and average number of stops, and increase the average travel speed for vehicles on the arterial road effectively.     

基于分布式实时机制的车载网络出行计划算法*

提出了一种基于分布式实时交通统计的机制,每辆汽车能够分布式地实时获取各路段的汽车通行时间,这样汽车就能够根据这些信息计算出行计划。仿真结果显示该方法对各路段交通情况评估具有有效性和信息扩散的实时性。     

Bi-level Decision-making Modeling for an Autonomous Driver Agent: Application in the Car-following Driving Behavior

ABSTRACT

Road crashes are present as an epidemic in road traffic and continue to grow up, where, according to World Health Organization; they cause more than 1.24 million deaths each year and 20 to 50 million non-fatal injuries, so they should represent by 2020 the third leading global cause of illness and injury. In this context, we are interested in this paper to the car-following driving behavior problem, since it alone accounts for almost 70% of road accidents, which they are caused by the incorrect judgment of the driver to keep a safe distance. Thus, we propose in this paper a decision-making model based on bi-level modeling, whose objective is to ensure the integration between road safety and the reducing travel time. To ensure this objective, we used the fuzzy logic approach to model the anticipation concept in order to extract more unobservable data from the road environment. Furthermore, we used the fuzzy logic approach in order to model the driver behaviors, in particular, the normative behaviors. The experimental results indicate that the decision to increase in velocity based on our model is ensured in the context of respecting the road safety.     

城市道路建设时序决策的鲁棒优化

为提高城市道路建设时序决策的鲁棒性,提出了城市道路建设时序决策优化的双 层规划模型。模型假定出行需求在一定范围内扰动,上层规划是在有限资金的约束下寻求各建设阶段的系统总出行时间与系统总出行时间对出行需求的灵敏度之间的综合最小值,下层规划为各建设阶段的随机用户均衡配流。文中推导出了系统总出行时间对出行需求灵敏度的计算式,并给出了模型的求解算法。最后以一个测试路网为例,对基于系统总出行时间、基于灵敏度、基于系统总出行时间与灵敏度综合出行时间的决策优化模型进行了计算分析,结果显示3种决策优化模型均可寻求到各自目标最优的城市道路建设时序,但在需求不确定的情景下基于灵敏度、基于系统总出行时间与灵敏度综合出行时间的决策优化结果更具鲁棒性。