The combination of continuous network design and route guidance

In this study, a traffic management measure is presented by combining the route guidance of Advanced Traveler Information System (ATIS) and the continuous network design (CNDP) to alleviate increasing traffic congestion. The route guidance recommends the travelers to choose the shortest path based on marginal travel cost and user constraints. The problem is formulated into a bi-level programming problem. The most distinct property of this problem formulation is that the feasible path set of its lower-level problem is determined by the decision variable of upper-level problem, while in conventional transportation network design problems the feasible path set for lower-level traffic assignment problem is fixed to be all the viable paths between each specific origin-destination pair. The simulated annealing algorithm is improved to solve this bi-level problem. A path-based traffic algorithm is developed to calculate the lower-level traffic assignment problem under the route guidance. Compared to the results of conventional CNDP, the measure presented in this study can better improve the transportation network performance.     

Simulation-based dynamic traffic assignment for short-term planning applications

Evaluation of Intelligent Transportation Systems (ITS) at the planning level requires the use of appropriate tools that can capture the dynamic and stochastic interactions between demand and supply. The objective of this paper is to present a methodological simulation-based framework for such applications and implement it in the context of dynamic traffic assignment. The framework consists of a mesoscopic supply simulator and a demand simulator that combines OD estimation capabilities with discrete travel behavior models. Simulation-based DTA systems are particularly suited to evaluate a wide range of Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). The simulation model performance is illustrated through two large-scale case studies in Irvine, California, and Lower Westchester County, NY.     

A Hybrid Route Choice Model for Dynamic Traffic Assignment

Network user equilibrium or user optimum is an ideal state that can hardly be achieved in real traffic. More often than not, every day traffic tends to be in disequilibrium rather than equilibrium, thanks to uncertainties in demand and supply of the network. In this paper we propose a hybrid route choice model for studying non-equilibrium traffic. It combines pre-trip route choice and en-route route choice to solve dynamic traffic assignment (DTA) in large-scale networks. Travelers are divided into two groups, habitual travelers and adaptive travelers. Habitual travelers strictly follow their pre-trip routes which can be generated in the way that major links, such as freeways or major arterial streets, are favored over minor links, while taking into account historical traffic information. Adaptive travelers are responsive to real-time information and willing to explore new routes from time to time. We apply the hybrid route choice model in a synthetic medium-scale network and a large-scale real network to assess its effect on the flow patterns and network performances, and compare them with those obtained from Predictive User Equilibrium (PUE) DTA. The results show that PUE-DTA usually produces considerably less congestion and less frequent queue spillback than the hybrid route choice model. The ratio between habitual and adaptive travelers is crucial in determining realistic flow and queuing patterns. Consistent with previous studies, we found that, in non-PUE DTA, supplying a medium sized group (usually less than 50%) of travelers real-time information is more beneficial to network performance than supplying the majority of travelers with real-time information. Finally, some suggestions are given on how to calibrate the hybrid route choice model in practice to produce realistic results.     

Research on Combined Dynamic Traffic Assignment and Signal Control

This paper presents a generalized bi-level programming model of combined dynamic traffic assignment and traffic signal control,and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO (first-in-first-out)rule.The chaotic optimal algorithm proposed in this paper can not only present the optimal signal settings,but also calculate,at each interval,the link inflow rates and outflow rates for the dynamic user optimal problem,and provide real-time information for the travelers.Finally,a numerical example is given to illustrate the application of the proposed model and solution algorithm, and comparison shows that this model has better system performance.     

动态交通分配与信号控制的组合模型及算法研究

This paper presents a generalized bi-level programming model of combined dynamic traffic assignment and traffic signal control, and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO (first-in-first-out) rule. The chaotic optimal algorithm proposed in this paper can not only present the optimal signal settings, but also calculate, at each interval, the link inflow rates and outflow rates for the dynamic user optimal problem, and provide real-time information for the travelers. Finally, a numerical example is given to illustrate the application of the proposed model and solution algorithm, and comparison shows that this model has better system performance.     

Real-time road traffic state prediction based on ARIMA and Kalman filter

The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.     

RHODES to intelligent transportation systems

To help fulfill the promises of ITS (intelligent transportation system), the ATLAS (Advanced Traffic and Logistics Algorithms and Systems) research center is developing and testing the RHODES (real-time hierarchical optimized distributed effective system) traffic control system. We believe that RHODES play a major role in the realization of future Advanced Traffic Management Systems, a major component of ITS.     

交通出行诱导的离散Stackelberg动态博弈模型及其求解算法

针对交通出行诱导的实际需要,分析了道路交通管理者和道路使用者的博弈策略和博弈特征.通过道路使用者信息的模糊模型.建立了交通出行诱导的离散动态Stackelberg博弈模型.分析了交通出行诱导的两阶段博弈特征,提出了基于逆向归纳法的博弈模型求解算法.通过对模型无约束转化,运用Monte-Carlo法对示例路网进行了计算机求解,求解结果表明产生的交通出行诱导方案能够实现系统最优下的用户最优.     

日常出行中的交通信息有效性仿真研究

采用计算机仿真的方法时交通信息在日常出行中的有效性问题进行了研究。论文在提供信息条件下出行选择行为研究的基础上建立了仿真模型,并通过该模型分析了在不同交通信息准确度、交通系统稳定性、交通信息提供方案条件下,交通信息的有效性。仿真结果表明。交通系统越不稳定,提供的交通信息准确度越高,交通信息的有效性越高;发布交通信息要有选择性．并不是交通信息越多交通信息的作用越大。研究成果时于我国智能交通运输系统建设有一定的指导意义。     

Drivers’ Performance Examination using a Personalized Adaptive Curve Speed Warning: Driving Simulator Study

ABSTRACT

A great number of traffic accidents occur during curve negotiation maneuvers. Most of these accidents could be avoided if drivers are provided with information that better guide them through the maneuver. Advanced Driver Assistance Systems (ADAS) such as Curve Speed Warning (CSW) have shown to be effective to improve safety on curve maneuvers by warning drivers of the speed required to make a safe curve maneuver. The effectiveness of such warning systems can potentially improve if the warnings are not only adapted to road and traffic condition but also adapted to individual drivers’ behavior. In this study, an Adaptive Curve Speed Warning (ACSW) system is developed that presents drivers a two-level visual and audio warning considering the variation in individual drivers’ perception-reaction time (PRT). The warning timing is adjusted according to a reward/punishment function to reinforce safer actions while providing an individualized in-time warning. Next, within a driving simulator environment, drivers’ performance using ACSW is compared to a CSW that does not consider PRT variation among drivers. Further, variation in drivers’ performance on curve maneuvers is discussed with respect to drivers’ approaching speed, variation in PRT, and braking behavior. Results show that drivers’ interaction with warning systems varies significantly based on their age and gender. In addition, results show how drivers approaching speed to a curve varies significantly based on road characteristics such as curve advisory speed and curve direction. Results from this study contribute to the development of more intelligent ADAS that could improve drivers’ comfort and safety.     

基于路段元胞传输模型的动态用户最优配流问题

利用基于路段的元胞传输模型进行模拟, 给出了一种计算实际路段出行阻抗的方法, 并在此基础上构造了基于路段变量的动态用户最优变分不等式模型. 模型采用针对迄节点的路段变量, 在每一个小时段都能给出路段流入率、流出率、路段流量和实际路段阻抗, 为用户提供较为全面的诱导信息打下了较好的理论基础. 采用了修正投影算法来进行求解. 数值算例表明模型具有的实用性和优越性, 使道路交通流宏观模型与动态网络交通配流问题得到较好的结合.     

Simulation and graphical animation of advanced manufacturing systems

Automated production systems, known as Flexible Manufacturing Systems (FMS), offer a promising solution for the low productivity of mid-volume, mid-variety batch production. These systems consist of integrated machine modules and material handling equipment under computer control.This paper describes a general purpose discrete-event Flexible Manufacturing Systems Simulator (FMSSIM). The package is programmed in FORTRAN and is capable of simulating different configurations, material handling systems, and topologies including bidirectional tracks. The simulator checks blockage of routes due to interference of carts and simulates random failures and repairs of the various components in the system. The simulator provides the user with a wide range of priority rules to select from, and enables the user to define his own rules if required. The simulator produces reports on various vital system performance statistics and also displays the movement of parts through the system on a refreshed CRT. This simulator is a modular, user-oriented package which allows the designer to evaluate a wide range of systems with varied design parameters and select an efficient flexible manufacturing system. Some general trends in the behaviour of the flexible manufacturing systems are identified and guidelines for their design and operation are suggested.     

Situation awareness assessment in critical driving situations at intersections by task and human error analysis

The rapid development of sensor and tracking technology enables deployment of new Advanced Driver Assistance Systems (ADAS) that support the driver not just on highways but in urban areas as well. Intersections particularly present critical traffic scenarios where almost 35% of accidents occur, partially due to the present lack of in‐depth research about human errors and their determinants. The first step in ergonomic design of ADAS is to identify the specific situations in which drivers require support. To contribute to identification of such spots, situation awareness of 20 drivers in four critical intersection scenarios was explored. The study was conducted in the fixed‐base driving simulator. The applied approach consisted of assessing drivers' expectations and mental workload and of comparing theoretically correct cognitive behavior to experimentally collected data. Intersection scenarios were divided into five segments, and for each segment a task analysis was made. The study has shown that the driving simulator environment can be successfully deployed to provoke and explore various driver errors. The results have revealed that, in scenarios in which information is objectively missing, the majority of errors happened because the drivers had inaccurate mental models of particular scenarios. To the contrary, in the complex scenario the major cause of accidents was information overload. Furthermore, the task analysis disclosed applicable areas of intersection assistance. © 2010 Wiley Periodicals, Inc.     

Simulation of distributed processing networks

A discrete event simulator named DDPSIM for distributed processing networks is presented. The simulator which was initially developed to support the design of a distributed processing system for an air defense application, has continued to evolve to accommodate a more general class of bus-connected networks in which software modules, or tasks, are distributed. It has also been used to model a complex air traffic control application which involved multiple networks. The modular organization and various distributed network component models of the simulator are described in detail. The simulator provides user friendly interfaces that include simple input conventions for system definition, and tabular and graphic outputs for comprehensive analyses of the simulation results. A relatively complex distributed processing system for air traffic control is used as an example to illustrate the simulation procedure and to show the simulation results. To conclude, the lessons learned from the experience in simulation are discussed.     

Traffic Dynamics and Mode Choice’s Delay Effect Under Traffic Restriction in Two-Mode Networks

Day-to-day traffic assignment models provide a powerful tool to analyze the time evolution of traffic flow within transport networks. In day-to-day dynamics, most models assume that there are only private cars on traffic networks and travelers update their choices each day. However, there are mainly two traffic modes, i.e. cars and buses, and travelers may not change their mode choices frequently. In this paper, a period-to-period traffic assignment model is proposed to capture traffic dynamics under traffic restrictions by considering interactions among different modes and introducing a delay about travelers’ mode choice. The existence and uniqueness of the equilibrium are analyzed. The linear stability of the equilibrium is analyzed and the eigenvalue range of Jacobian matrix is estimated by using the Gershgorin circle theorem. Both theoretical analyses and numerical simulations indicated that compared to the system with no mode choice, mode choice behaviors could improve the stability of the equilibrium. Additionally, mode choice behaviors with delay might further increase the stability domain and dampen oscillations on the cost and the flows. This study explains the evolution patterns of transportation networks under traffic policy and provides guidance for network design and management.

     

基于出行需求的城市车辆共享模式演化分析

车辆共享从资源分配的角度提高了汽车资源利用率.为了激励车辆共享,有关出行需求和出行者体验的研究势在必行.通过DBSCAN算法测量了城市内的交通需求聚集度,验证了车辆共享的可行性.确定了影响用户效用的关键因素,提出了基于Logit模型的数学模型以描述出行者体验和汽车资源利用率,预测出行者选择.同时,利用真实数据和调查结果,使测量和模型更加准确、真实.通过仿真实验,观测并分析了交通模式的演化过程及结果,发现城市出行需求量和交通需求聚集度是影响交通模式演化的主要因素.出行需求量需达到一定数值,演化才能达到稳定状态.城市出行交通需求聚集度越高,车辆共享的参与者则越多,其所获效用也越高.当出行需求量大于290,且交通需求聚集度大于0.9时,所有出行者都将选择车辆共享.最后,基于神州专车的出行数据,实验并观测了北京市交通模式的演化,发现若要在不加入经济因素或政策干预的情况下实施车辆共享,其交通模式将无法达到稳定状态.     

基于手机RFSIM与北斗GIS城市实时交通动态导航平台

目前,从智能交通系统的发展（ITS）来看,国内大多数导航系统为静态导航,缺乏根据实时交通信息进行动态导航的功能.从而提出一种基于手机RFSIM与GIS技术结合的城市实时交通动态导航平台,为出行者提供实时动态的交通信息和导航服务.该系统主要包括实时交通信息采集系统,短时交通预测系统,北斗或GPS定位以及GIS实时导航系统.在已获得的相关技术的支撑下,着力解决在此导航系统中涉及到的交通信息的获取、北斗定位和实时交通动态导航中的关键技术问题,并进行可行性分析.