Optimizing sustainable feeder bus operation considering realistic networks and heterogeneous demand

A mathematical model is developed to optimize social and fiscal sustainable operation of a feeder bus system considering realistic network and heterogeneous demand. The objective total profit is a nonlinear, mixed integer function, which is maximized by optimizing the number of stops, headway, and fare. The stops are located which maximize the ridership. The demand elasticity for the bus service is dependent on passengers' access distance, wait time, in‐vehicle time, and fare. An optimization algorithm is developed to search for the optimal solution that maximizes the profit. The modeling approach is applied to planning a bus transit system within Woodbridge, New Jersey. Copyright © 2011 John Wiley & Sons, Ltd.     

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An adaptive model for highway travel time prediction

Traffic congestion caused by either insufficient road capacity or unexpected events has been a major problem in urban transportation networks. To disseminate accurate traveler information and reduce congestion impact, it is desirable to develop an adaptive model to predict travel time. The proposed model is practically implementable to capture dynamic traffic patterns under various conditions, which integrates the features of exponential smoothing and the Kalman filter by utilizing both real‐time and historic data. The model is simple in formulation while robust in performance in terms of accuracy and stability. With a constraint or nonconstraint smoothing factor, the proposed model is tested with both real world and simulated data and demonstrated itself a sound model that outperforms others (e.g., Kalman filter and simple exponential smoothing) specifically under recurring and nonrecurring congestion. Copyright © 2012 John Wiley & Sons, Ltd.     

Participation and desire: leisure activities among Canadian adults with disabilities

As a signatory of the Convention on the Rights of Persons with Disabilities, Canada has assumed the obligation to foster the fundamental right of individuals with impairments to participate in all normal activities and relationships of society. Currently, however, relatively little is known about the extent to which Canadians with disabilities are able to exercise of this right. Previous research has investigated the employment status and commute distance of persons with disabilities in Canada. In this paper, we use the 2006 Participation and Activity Limitation Survey to investigate, from the perspective of Canadian adults with disabilities, the factors that influence (1) their frequency of participation in various leisure and recreation activities; (2) the role of transportation in activity participation; and (3) the desire for more leisure and recreation activities during spare time. The latter question is closely associated to the experience of exclusion. The results of the analysis indicate that some of the very factors that tend to reduce participation also relate to a desire for more leisure and recreation activities.     

Developing freeway lane‐changing support systems using artificial neural networks

Lane‐changing involves many concerns about safety and efficiency which makes it one of the most difficult tasks of driving. It is indeed quite personal since drivers operate vehicles according to their integrated perception of comprehensive circumstances rather than individual rules. A lane‐changing decision support model is developed in this study using artificial neural networks (ANN). The advantages of the ANN approach lie in the learning capability. Due to its nature, an ANN model can consolidate various kinds of information surrounding the vehicle for the drivers and generate reliable results to help control vehicles. It then becomes a useful mechanism to assist drivers in judging current situations and making the right decisions. Several preliminary validations and comparisons are conducted with the field survey data. It is confirmed that the ANN model mimics traffic characteristics more accurately than conventional methods. This product would expedite the implementation of relevant applications in the intelligent transportation systems context. In particular, the ANN model can be adapted to individual driver characteristics. This reveals practical feasibility and significant market potential for customized in‐vehicle equipment.     

Automated Vehicle Merging Maneuver Implementation for AHS

Summary This paper presents a real-time implementation of a general merging algorithm for automated highway systems. A merging control problem is proposed first. A real-time algorithm is then presented, which is used to calculate a smooth reference speed trajectory for the merging vehicle based on the speed of the main lane vehicle. This algorithm can also be applied even when the main lane vehicles change speed. To make the algorithm adapt to different road layouts and to increase safety, a concept of virtual platooning is proposed. It effectively shifts the time of platoon formation forward prior to the start of real merging. Aspects closely related to real-time implementation are discussed, such as the controller adopted, the use of magnetometer based distance measurement and information passing by communication from main lane vehicles. Test results are presented and briefly analyzed.     

Using automatic passenger counter data in bus arrival time prediction

Artificial neural networks have been used in a variety of prediction models because of their flexibility in modeling complicated systems. Using the automatic passenger counter data collected by New Jersey Transit, a model based on a neural network was developed to predict bus arrival times. Test runs showed that the predicted travel times generated by the models are reasonably close to the actual arrival times.     

Patterns and controls of chlorophyll-a and primary productivity cycles in the Southern California Bight

Factors that influence the magnitude and the depth of the chlorophyll maximum layer in the ocean off Southern California are explored using observations from the long-term California Cooperative Oceanic Fisheries Investigations (CalCOFI) program. The data record is sufficiently long to reveal patterns not always evident from single stations or single cruises. Processes such as coastal wind-driven upwelling, geostrophic circulation, and annual physical and chemical cycles are illustrated to demonstrate their effect on euphotic zone nutrient availability, and subsequent phytoplankton biomass and primary productivity. In this area, where the influence of wind-driven upwelling is spatially restricted and advected waters are generally nutrient depleted, geostrophically induced upwelling and winter convection become important in determining spatial and temporal patterns of phytoplankton.     

Optimization Of Headway,Vehicle Size and Route Choice for Minimum Cost Feeder Service

ABSTRACT

Many people use public transportation systems to reach their destination, while others use personal vehicles. Poor transportation systems do not attract ridership. Therefore, the usage of passenger cars increases, and traffic and environmental conditions deteriorate. Efficient public transportation has been recognized as one of the potential ways of mitigating air pollution, reducing energy consumption, improving mobility and alleviating traffic congestion. The objective of this study is to optimize a bus feeder service that provides the shuttle service between a recreation center (e.g. Sandy Hook, NJ) and a major public transportation facility, subject to site-specific constraints such as vehicle schedules, bus availability, service capacity and budget. The decision variables include bus headway, vehicle size and route choice. The solution methodology integrating both analytical and numerical techniques is developed, which optimizes the decision variables. Finally, the proposed solution methodology is applied to a case study. Numerical results, including optimal solutions and sensitivity analyses, are presented while the level of coordination between the feeder service and a major transportation service is discussed.     

考虑车辆随机到站时间的动态需求响应型接驳公交线路优化

车辆到站时间的不准时性严重影响着需求响应型公交的服务水平和乘客选择公共交通的出行意愿,因此,本文对考虑车辆随机到站时间的动态需求响应型接驳公交线路优化问题进行研究。以运营商成本、乘客乘车时间成本、乘客等待时间成本组成的系统总成本最小为目标建立数学模型,通过优化车辆路径寻求系统总成本最优的需求响应型接驳公交服务方案,其创新之处在于,在服务过程中允许乘客提交实时出行需求;定义车辆到站时间服从已知分布以描述其随机性。提出一种遗传算法和邻域搜索相结合的启发式算法对模型进行求解,该算法融合了遗传算法的全局搜索优势和邻域搜索的局部搜索能力,通过算例测试分析对本文算法的有效性及先进性进行验证。最后,基于西安市延平门地铁站设计数值实验,结果表明,考虑车辆随机到站时间可以在一定程度上减少乘客时间成本和系统总成本。