Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

罕遇地震作用下高速铁路减隔震简支梁桥合理计算模型的探讨

以往在分析减隔震桥梁的地震响应时,由于考虑到桥墩和基础应保持弹性工作状态,在基于强度的设计中偏于安全考虑桥墩一般采用毛截面刚度建立弹性梁单元模型。实际上,在罕遇地震作用下,桥墩墩底截面虽然未达到屈服状态,仍然会出现保护层混凝土开裂,并导致桥墩刚度降低。此时,应考虑对桥墩刚度进行适当修正以估计桥梁的各项地震响应参数,这也有利于实现减隔震桥梁基于位移的抗震设计。结合西部高速铁路中典型的简支梁桥结构形式,分别采用弹塑性纤维梁柱单元、弹性梁柱单元、考虑刚度修正的弹性梁柱单元模拟桥墩建立3种计算模型,探讨适用于罕遇地震作用下的高速铁路减隔震桥梁的合理计算模型。结果表明,当罕遇地震作用下桥墩位移延性超过0.5时,考虑刚度修正的弹性梁柱单元模拟桥墩的计算模型能够较好地估计桥梁各项地震响应参数。     

学院教职工付酬方案的最优化控制系统

利用线性优化理论和教学建模方法，对学院教职工最优化付酬方案进行了研究了最优化分配教职工报酬的控制模型，为主管部门提供了强有力的参考。     

Passenger waiting time and information acquisition using automatic vehicle location for verification

Bus riders utilize a variety of information media to learn how to travel to their destinations and to learn when they should arrive at bus stops. As part of the OCTA (Orange County Transit Authority) Transit Probe evaluation, 1199 passengers were surveyed to measure relationships between information acquisition and waiting time. A unique aspect of the survey was that some of the data could be correlated with automatic‐vehicle‐location (AVL) measurements of bus lateness at stops. Insights are provided as to the types of information riders acquire based on the nature of the trip and demographic characteristics. Insights are also provided as to factors affecting perceived waiting time. We found age group, whether a person needs to arrive at a destination by a specific time, primary language, and whether the person is a first‐time user of the bus line to be significant causal factors.     

高速公路左侧路肩设置必要性研究

尽管<公路工程技术标准>(JTG B01-2003)中规定八车道的高速公路应当设置2.5 m的左侧硬路肩,但国内几条刚刚改建完成的八车道高速公路中没有一条设置了左侧硬路肩,甚至没有宽于0.75 m的路缘带.研究运用可接受间隙理论计算了不同流量和车道数条件下车辆从最内侧车道穿插变道停驶到右侧路肩这一过程的概率和所需距离,并通过交通事故数据调研,分析了左侧路肩设置对提高交通安全的影响,从运行效率和交通安全两个方面定性和定量分析了左侧路肩设置的必要性.     

半刚性路面基层养生期的试验研究

分析了缩短半刚性路面基层养生的机理,解决了半刚性路面基层常规7d养生期耗费大、工期长、遇雨行车影响路面平整度和质量,因洒水养生干、湿交替产生的干缩裂缝,与沥青面层结合不好、抗剪力差、行车易产生推移变形等技术难题.降低了工程造价,取得明显的社会、经济效益.     

Incorporating Driver Behaviors in Network Design Problems: Challenges and Opportunities

The goal of a network design problem (NDP) is to make optimal decisions to achieve a certain objective such as minimizing total travel time or maximizing tolls collected in the network. A critical component to NDP is how travelers make their route choices. Researchers in transportation have adopted human decision theories to describe more accurate route choice behaviors. In this paper, we review the NDP with various route choice models: the random utility model (RUM), random regret-minimization (RRM) model, bounded rationality (BR), cumulative prospect theory (CPT), the fuzzy logic model (FLM) and dynamic learning models. Moreover, we identify challenges in applying behavioral route choice models to NDP and opportunities for future research.     

Roads in transition: Integrated modeling of a manufacturer-traveler-infrastructure system in a mixed autonomous/human driving environment

This paper develops an integrated model to characterize the market penetration of autonomous vehicles (AVs) in urban transportation networks. The model explicitly accounts for the interplay among the AV manufacturer, travelers with heterogeneous values of travel time (VOTT), and road infrastructure capacity. By making in-vehicle time use more leisurely or productive, AVs reduce travelers’ VOTT. In addition, AVs can move closer together than human-driven vehicles because of shorter safe reaction time, which leads to increased road capacity. On the other hand, the use of AV technologies means added manufacturing cost and higher price. Thus, traveler adoption of AVs will trade VOTT savings with additional out-of-pocket cost. The model is structured as a leader (AV manufacturer)-follower (traveler) game. Given the cost of producing AVs, the AV manufacturer sets AV price to maximize profit while anticipating AV market penetration. Given an AV price, the vehicle and routing choice of heterogeneous travelers are modeled by combining a multinomial logit model with multi-modal multi-class user equilibrium (UE). The overall problem is formulated as a mathematical program with complementarity constraints (MPCC), which is challenging to solve. We propose a solution approach based on piecewise linearization of the MPCC as a mixed-integer linear program (MILP) and solving the MILP to global optimality. Non-uniform distribution of breakpoints that delimit piecewise intervals and feasibility-based domain reduction are further employed to reduce the approximation error brought by linearization. The model is implemented in a simplified Singapore network with extensive sensitivity analyses and the Sioux Falls network. Computational results demonstrate the effectiveness and efficiency of the solution approach and yield valuable insights about transportation system performance in a mixed autonomous/human driving environment.     

Estimating the bus user time benefits of implementing a median busway: Methodology and case study

This paper presents a general framework to estimate the bus user time benefits of a median busway including the effects on travel time and access time. Unlike previous models, we take into account the effects of geometry and the interaction with the demand structure. Models for predicting the bus in-vehicle time benefits of a median dual carriageway busway against mixed traffic condition on 2 and 3 lanes roads are estimated using data from a case study in Santiago (Chile), using a bus travel time model empirically estimated and considering different base case situations, including mixed traffic operations and bus lanes. Results of the application show that the expected in-vehicle time savings of a median busway might be reduced by access time losses due to increased walking distances and road crossing delays. Also, that net time benefits can vary significantly according to the base situation and the structure of demand considered. These findings point out to the need of including a wider set of impacts when studying the benefits of median busways, beyond in-vehicle time savings only. The empirical work presented here is completely based on passive data coming from GPS and smartcards, what makes easier and cheaper to conduct this type of analysis as well as to do it with a comprehensive scope at an early stage of the development of a BRT project. This framework can be extended to other types of dedicated bus lanes provided that a corresponding bus travel time savings model is available.