Evaluation of transit proximity effects on residential land prices: an empirical study in Austin,Texas

The Austin MetroRail line in Texas, U.S.A. began operations in 2010 and since then transit-oriented development projects have been fuelled around train stations. Traditional location theory holds that proximity to a transit station should be capitalized into land prices, and numerous empirical studies have demonstrated the effects with mixed results. However, to date no empirical study has investigated this relationship between the newly built rail line and land prices in Austin. This study fills the research gap and suggests a positive effect associated with transit proximity by employing spatial hedonic models. This study also contributes to the existing literature especially in the context of Texas cities where there is a lack of relevant research on transit-capitalization-related topics, and provides insights for decision-makers with different perspectives regarding further rail investment or transportation financing strategies such as value capture programs.     

基于神经网络的智能驾驶模式识别研究

为满足智能驾驶汽车高级驾驶辅助系统(ADAS)功能研发和验证的需求,提高ADAS功能的准确性,设计了一款基于神经网络的智能驾驶模式识别程序,该程序由数据采集、目标检测、场景识别预测3个模块组成.数据采集模块利用ESR毫米波雷达、前置摄像头对交通环境及周围车辆的数据信息进行采集;目标监测模块通过控制算法选择判断触发各类A...     

An analysis of public bus transit performance in Indian cities

Maintaining and enhancing public transit service in Indian cities is important, to meet rapidly growing mass mobility needs, and curb personal motor vehicle activity and its impacts at low cost. Indian cities rely predominantly on buses for public transport, and are likely to continue to do so for years. However, the public bus transit service is inadequate, and unaffordable for the urban poor. The paper explores the factors that contribute to and affect efforts to improve this situation, based on an analysis of the financial and operational performance of the public bus transit service in the four metropolitan centres and four secondary cities during the 1990s. Overall, there were persistent losses, owing to increasing input costs and declining productivity. The losses occurred despite rapidly increasing fares, and ridership declined. The situation, and the ability to address it, is worse in the secondary cities than the metropolitan centres. We suggest a disaggregated approach based on the needs and motivations of different groups in relation to public transit, along with improved operating conditions and policies to internalize costs of personal motor vehicle use, to address the challenge of providing financially viable and affordable public bus transit service.     

Recognizing metro-bus transfers from smart card data

Transfer points between metro and bus services remain an elusive, yet critical junction for transportation practitioners. Based on massive Smart Card (SC) data, previous studies apply a one-size-fits-all criterion to discriminate between transfers. However, this is not sufficiently convincing for different transfer pairs. To counter this problem, this study applies an association rules algorithm and cluster analysis to recognize metro-to-bus transfers using SC data, and demonstrates transfer recognition in a case study based on SC data collected during a week in Nanjing, China. It is shown that 85% of the transfer-recognition results are quite stable through the whole week, and the median transfer time between metro and bus is below 20?min. The method proposed in this study can be used to identify the busiest transfer points and to obtain average transfer times, which facilitates a smarter and more efficient public transit network.     

Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.     

Does the increase of public transit fares deteriorate air quality in Beijing?

Since 2006, Beijing lowered its public transit fares as a way to improve air quality. However, Beijing increased public transportation fare prices from December 28, 2014, and commuters pay for the distance they traveled rather than a flat fare. This paper explores the effect of Beijing public transit fares increase on air quality. We collect daily data of air pollution and weather variables and use synthetic control method of Abadie and Gardeazabal (2003) to select control units. We then estimate a difference-in-differences model and assess the effect of the policy on air quality index (AQI). We find a 16.28% increase in air pollution in short run. However, we find no longer-run effect on air quality.     

Optimization of bus stop placement for routes on uneven topography

The improvement and expansion of public transport is an increasingly important solution to the high congestion costs and worsening environmental impacts of the car dominated transport systems seen in many cities today. The intelligent design of stop locations is one way to improve the quality of PT and thereby improve its ridership. Stop placement is a relatively complex task as it involves a trade-off between two competing goals; accessibility and operation; however this trade-off can be made explicit using an appropriate mathematical model. Many such models have been developed in the literature, however none consider the effects of uneven topography. Topography is an important but often neglected factor in the design of public transportation systems, with the potential to have a significant impact on the accessibility, operation and planning of a transit service. In this work a mathematical modelling approach to bus stop placement is developed which includes considerations of uneven topography in three ways; (1) Its effect on walking speed; (2) Its impact on the attractiveness of an access path to a transit service; and (3) Its effect on acceleration rates at stops. Because of the complexity of the model developed, a heuristic evolutionary algorithm’ is employed to approximate an optimal solution to the model. Finally, the model and solution method are applied to a case study in the Auckland CBD area in New Zealand.     

Extraboard team sizing: An analysis of short unscheduled absences among regular transit drivers

Several factors contribute to short-duration unscheduled absences of bus transit drivers. This article aims to understand these factors at the aggregate level and to anticipate future total absence that will need to be filled for a large-size transit operator. The aggregate level is defined as the total number of regular driver absences per garage, day of week and time period that need to be covered by the extraboards. This study analyzes absenteeism data obtained from OC Transpo, the transit provider of the city of Ottawa, Canada. A multilevel regression model is generated to investigate regular drivers’ absences. The short-unscheduled absence is estimated in relation to temporal factors, drivers’ personal characteristics, aspects of assigned work, and service delivery characteristics. Furthermore, using the model’s coefficients, sensitivity analyses are conducted to demonstrate the advantages of this technique over traditional ones adopted by various transit agencies. This study provides transit planners and policy makers with a practical methodology that can be used to support extraboard planning practice and help reduce the incidence of missed trips due to absences while having the appropriate size of extraboard drivers.     

Including passengers’ response to crowding in the Dutch national train passenger assignment model

Transit passengers’ response to crowded conditions has been studied empirically, yet is limitedly included in transport models currently used in the design of policy and infrastructure investments. This has consequences for the practical applicability of these models in studies on, for instance, timetabling, train capacity management strategies, project appraisal, and passenger satisfaction. Here we propose four methods to include the effect of crowding, based on existing studies on passengers’ perception and response as well as often-used crowding indicators. These four alternative methods are implemented in the train passenger assignment procedure of the Dutch national transport model, and evaluated with respect to their impacts on the model results for the Dutch railway network. The four methods relate to four different ways in which an additive trip penalty and/or time-multiplier can be incorporated in the train utility function for different travel purposes, to capture the disutility of crowding as measured by the load factor. The analyses of the test case favor the hybrid method using both a boarding penalty (capturing seat availability upon boarding) and a time-multiplier (capturing physical comfort and safety throughout the trip). This method produces consistent results, while the additional computational effort that it imposes is acceptable. Further empirical underpinning is needed to conclusively show which of these methods best captures passengers’ response behavior quantitatively (for different travel purposes and conditions).     

Assessing social equity in distance based transit fares using a model of travel behavior

The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal/continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare distance travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe.