An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

城市道路瓶颈交通调节策略研究

根据道路瓶颈的形成机理及主要特征,提出瓶颈交通汇入量控制策略、诱导分流策略及需求调节策略,并构建相应的调节流程,对解决城市瓶颈交通问题有一定的指导作用.     

Stated choices and benefit estimates in the context of traffic calming schemes: Utility maximization,regret minimization,or both?

This paper proposes a discrete mixture model which assigns individuals, up to a probability, to either a class of random utility (RU) maximizers or a class of random regret (RR) minimizers, on the basis of their sequence of observed choices. Our proposed model advances the state of the art of RU–RR mixture models by (i) adding and simultaneously estimating a membership model which predicts the probability of belonging to a RU or RR class; (ii) adding a layer of random taste heterogeneity within each behavioural class; and (iii) deriving a welfare measure associated with the RU–RR mixture model and consistent with referendum-voting, which is the adequate mechanism of provision for such local public goods. The context of our empirical application is a stated choice experiment concerning traffic calming schemes. We find that the random parameter RU–RR mixture model not only outperforms its fixed coefficient counterpart in terms of fit—as expected—but also in terms of plausibility of membership determinants of behavioural class. In line with psychological theories of regret, we find that, compared to respondents who are familiar with the choice context (i.e. the traffic calming scheme), unfamiliar respondents are more likely to be regret minimizers than utility maximizers.     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.     

Stop-and-go traffic analysis: Theoretical properties,environmental impacts and oscillation mitigation

This study aims (i) to analyze theoretical properties of a recently proposed describing-function (DF) based approach (Li and Ouyang, 2011; Li et al., 2012) for traffic oscillation quantification, (ii) to adapt it for estimating fuel consumption and emission from traffic oscillation and (iii) to explore vehicle control strategies of smoothing traffic with advanced technologies. The DF approach was developed to predict traffic oscillation propagation across a platoon of vehicles following each other by a nonlinear car-following law with only the leading vehicle’s input. We first simplify the DF approach and prove a set of properties (e.g., existence and uniqueness of its solution) that assure its prediction is always consistent with observed traffic oscillation patterns. Then we integrate the DF approach with existing estimation models of fuel consumption and emission to analytically predict environmental impacts (i.e., unit-distance fuel consumption and emission) from traffic oscillation. The prediction results by the DF approach are validated with both computer simulation and field measurements. Further, we explore how to utilize advantageous features of emerging sensing, communication and control technologies, such as fast response and information sharing, to smooth traffic oscillation and reduce its environmental impacts. We extend the studied car-following law to incorporate these features and apply the DF approach to demonstrate how these features can help dampen the growth of oscillation and environmental impact measurements. For information sharing, we convert the corresponding extended car-following law into a new fixed point problem and propose a simple bisecting based algorithm to efficiently solve it. Numerical experiments show that these new car-following control strategies can effectively suppress development of oscillation amplitude and consequently mitigate fuel consumption and emission.     

Public transportation development and traffic accident prevention in Indonesia

Traffic accidents have long been known as an iceberg for comprehending the discrepancies of traffic management and entire transportation systems. Figures detailing traffic accidents in Indonesia, as is the case in many other countries, show significantly high numbers and severity levels; these types of totals are also evident in Jakarta, the highest-populated city in the country. While the common consensus recognizes that traffic accidents are the results of three different factor types, namely, human factors, vehicle factors, and external factors (including road conditions), human factors have the strongest influence—and figures on a worldwide scale corroborate that assertion. We, however, try to pinpoint the issues of non-human factors in light of increasing traffic accidents in Indonesia, where motorbike accidents account for the majority of incidents. We then consider three important pillars of action: the development of public transportation, improvement of the road ratio, and traffic management measures.     

基于节能减排的高速公路收费站处拥挤车流控制技术

针对高速公路收费站交通拥堵时车辆长时怠速且频繁启动导致油耗急剧增加的问题,提出了收费站拥堵车流交通控制系统.该系统通过设置车辆感应器防止收费亭空闲,利用车流控制灯将拥挤缓行的车流变为分批放行车流,综合考虑排队长度、收费通道数、收费广场长度、主线车道数、收费时间等因素,以排队系统中车辆油耗最低为目标,建立了车辆感应器和信号灯位置及信号灯时长等参数优化模型,从而实现了不增加车辆通行时间同时减少车辆怠速时间和停车次数的目的.最后结合实例,运用交通模拟技术对该控制系统效果进行了验证.结果表明:当收费站排队长度为2 000 m时,采用该控制系统可使车辆通过收费站的油耗降低90％.     

空域扇区流量与拥塞预测的概率方法

为了提高扇区流量预测准确度、减小扇区拥塞预测的虚警率,分析了影响空中交通的随机因素,建立了航空器进入扇区时刻、穿越扇区飞行时间和离开扇区时刻的概率分布模型.利用进入、离开扇区时刻的累积分布函数,计算航空器占用扇区的概率,并在此基础上,提出了基于Monte Carlo仿真的扇区拥塞预测概率方法.算例仿真结果表明:与确定型拥塞预测方法相比,采用概率预测方法可将拥塞时段比例的平均值从42%减少到33%.     

北京实行错峰上下班解决交通拥堵问题的分析探讨

城市交通拥堵治理问题,是当前我国乃至世界范围内的一个热门话题和难题。通过采取错峰上下班措施,实现道路交通负荷的错峰调节,对于城市的交通拥堵状况有显著效果。剖析北京市采取错峰上下班以达到缓解交通拥堵的措施,通过研究国内外实施成功的错峰上下班工作制,分析北京错峰上下班措施的实施条件,提出北京错峰上下班工作制的完善方案,对于解决交通拥堵问题具有积极的意义。