Journal of Public Transportation最新文献

We present a method oriented to determine indicators representing different dimensions of users' travel time, such as in-vehicle time and its variability. We focus the service level measurement on users, but not their subjective perception, since we build the indicators from objective data. We apply the method to six Latin American cities, which shows the method's flexibility to fit different available information collected from sources such as in-field measurements or electronic control systems for bus operations. The method's core is the selection of a representative sample of trips. We estimate the service level based on different variables (e.g., speed, frequency, travel time, and waiting time) for every sampled trip. The contribution of this work is the design and implementation of a methodology for service level evaluation. As a byproduct, we compare public transportation systems' levels of service in the studied cities.

Transantiago, the public transport system implemented in Santiago, Chile, remains a controversial subject of public debate due to constantly increasing fare evasion rates throughout its decade-long existence. The research question under consideration in this paper is what motivates individuals to evade or pay bus fares. To answer this question, we developed a multidisciplinary study that combined relevant engineering expertise with a sociological perspective and combined quantitative analyses with qualitative methodologies to include new variables and categories that previous studies on the subject have omitted. We formulated a survey, which was administered to 503 public transport system users. The data we obtained were analyzed using a factor analysis. As a result, six dimensions were identified that explain motivations for fare evasion: (1) behavior and social norms, (2) the public transport service’s image, (3) social acceptance of evasion, (4) trip planning, (5) anti-evasion methods, and (6) fear of law enforcement.

Bus systems cannot be fully explored if issues such as safety of bus passengers on-board or at bus stops are not addressed. This study was aimed at assessing the safety of bus stops in Ibadan metropolis. Twenty bus stops were purposefully selected for this study from the five urban local government areas in Ibadan metropolis. A field survey involving interviews with 50 passengers and direct observations of hazardous acts was carried out at the bus stops. Casual factors of hazardous acts were noted as well. A score survey was conducted with 17 experts (civil engineers and transportation engineering researchers) where they were asked to rate how much each casual factor contributes to its corresponding hazardous act using a scale of 1–4 (1 being ‘not important at all’ and 4 being ‘very important’). Experts were also asked to make pairwise comparisons among hazardous acts and consistent responses were analyzed using Analytic Hierarchy process (AHP). Results from the score survey and AHP were used to model the safety levels of the selected bus stops. With safety levels ranging from 2.38 to 4.83 (10 being the best and 0 being the worst), all bus stops fell short of an acceptable level of safety. Also, Interviews conducted revealed passengers’ dissatisfaction with their user-experience. Recommendations were therefore made based on the findings.

Multimodality in Public Transport has been proven to be one of the main drivers of sustainability and economic feasibility for the last few decades. Consequently, user satisfaction for transfers is the key to adequately serving demand. This research studies on commuters’ perception of comfort at interchanges, focusing on the connection between metro systems and other modes. Satisfaction analysis and modelling is conducted using weighted regression, factor analysis and ordered logit models for nine transfers at major interchanges in two Indian cities (New Delhi and Kolkata) and one German city (Munich); aiming at revealing the differences in user satisfaction in developing and developed economy, and for different Public Transport quality and interchanges. The results indicate that factors of transfer quality, accessibility and physical hindrances are significant in Indian case and the human factor, and transfer quality are significant in the case of Munich, Germany. Additionally, it is found that perceived comfort differs on commuters’ experiences with transfer distance and time.

Demand responsive feeder transit can minimize passengers’ travel times and operator’s costs by optimizing routing based on real demand. One question about the demand response feeder transit operation is whether it can be optimized with door-to-door service or with temporary stops for picking up and delivering passengers. Obviously, door-to-door service eliminates passengers’ walking distances, but it increases passenger in-vehicle travel times and vehicle operating distance and costs. On the other hand, demand responsive feeder transit with temporary stops, which designates the temporary locations picking up and dropping passengers, minimizes bus operating distance and time as well as passenger in-vehicle times, although it increases passengers’ walking distances and times. The developed model uses metaheuristic approaches, including two main algorithms; a passenger’s clustering algorithm based on Particle swarm optimization (PSO) approach and a vehicle routing algorithm that uses simulated annealing (SA) solving method. The algorithm developed an optimal algorithm for clustering and grouping of passengers considering their physical locations and time windows then it was integrated with the authors’ previously developed algorithm for the optimal flexible feeder bus routing as a mixed integer model that objects to minimize the total costs including both passengers traveling times and operator’s operating costs The results of this study showed that although feeder networks with temporary stops always lower operating costs and lessen in-vehicle travel time compared to those with a door-to-door option, the total costs and optimal routings are highly sensitive to the location of passengers.

Peer comparison is a valuable management tool for identifying a transit provider’s areas of relative strength, areas with room for improvement, and high-performing peers to learn from. While the U.S. Rural National Transit Database (NTD) provides performance data about rural and small city transit providers, methods and tools have been lacking for these providers to apply NTD data to identify appropriate peers. This paper describes the development of a rural transit peer-grouping method as a counterpart to a method previously developed and implemented for urban providers, and compares this method to previous transit peer-grouping approaches. In contrast to most previous efforts, which have produced fixed and often large peer groups, this method dynamically and transparently identifies appropriate peers for individual rural and small city transit providers. The method has been implemented in freely available web-based software.

This paper empirically demonstrates the value of quasi-experimental study designs to evaluate the direct impacts of new public transit services on ridership within its corridor. Using a new bus rapid transit (BRT) service, CMAX, in Columbus, Ohio, USA, as an example, we compare its impact on ridership based on a pre-post and quasi-experimental analysis framework. We conduct the pre-post analysis using a ridership space-time cube exploring a massive Automatic Passenger Counter (APC) database. Differences in total passenger counts before and after the BRT intervention indicate a 36% increase in ridership within its corridor. However, this patronage increase may not be attributable solely to the new public transit service. Potential confounding effects include systemwide ridership trends and a new unlimited transit pass program for downtown workers. To address these issues, we adopt a quasi-experimental study design with a difference-in-differences (DiD) identification strategy. We use propensity score matching (PSM) to match a counterfactual control group with the treatment group when implementing DiD model. After accounting for confounding effects, we find a less than 5% increase but not statistically significant impacts of CMAX on ridership. Our results support the argument that a simple pre-post analysis ignoring confounding effects can lead to a misleading evaluation of a new public transit service’s direct impact on ridership.

Understanding the factors and drivers of user satisfaction with public transportation (PT) systems has been a subject of research for decades, as it provides insight into influencing ridership increases. This research addresses user satisfaction using SEM-MIMIC models to analyze three PT bus subsystems in the same city (Bogotá Colombia): A Bus Rapid Transit, a formalized bus subsystem, and a semi-formalized one that operates simultaneously. After developing three independent models, we found the same three latent variables (LVs), namely subsystems “condition”, “service”, and “safety/security”, in each one. However, the strength and significance of the direct and indirect effects among the three LVs vary from one subsystem to another. In general, satisfaction is initially based on a person’s perception of the subsystem’s condition and ends with his/her perception of the service, with safety and service being the mediating variables to explain satisfaction. Unlike previous studies, we were able to identify relationships among these three independent variables for each subsystem, allowing us to assess the direct and indirect effects on overall subsystem satisfaction. This research provides decision-makers with a broader understanding of how infrastructure, vehicles, operational attributes, and regulation processes influence user satisfaction through perceptions and how they can help improve the PT service for its users.

Due to concerns about data quality, Automated Passenger Counting technology has rarely been used to analyze local ridership trends. This paper presents a novel framework to test the consistency and completeness of automated passenger count (APC) data in four cities. Weekday APC data are aggregated at the system level and compared with the National Transit Database between 2012 and 2018. In all four agencies, passenger counts closely follow the fluctuations observed in the national transit database. There is, however, a slight drift in two of the four agencies, possibly due to the diverging trends between weekday and weekend ridership. At the stop-level, missing and duplicate vehicle-trips are identified using schedule data from the General Transit Feed Specification. Missing and duplicate trips only concern a small proportion of stops, which can be eliminated using the proposed method. Overall, this research leads the way towards the analysis of factors affecting ridership on a tight spatial and temporal scale.

A terrorist attack on the public transportation system of a city can cripple its economy. Uninformed investments in countermeasures may result in a waste of resources if the risk is negligible. However, risks are difficult to quantify in an objective manner because of uncertainties, speculations, and subjective assumptions. This study contributes a probabilistic model, validated by ten different machine learning methods applied to the fusion of six heterogeneous datasets, to objectively quantify risks at different jurisdictional scales. The risk index is purposefully simple to quickly inform a proportional prioritization of resources to make fair investment decisions that stakeholders can easily understand, and to guide policy formulation. The main finding is that the risk indices among public transit jurisdictions in the United States distribute normally. This result enables agencies to evaluate the quality of their risk index calculations by detecting an outlier or a large deviation from the expected value.