Ehab Diab

Non-stop equity: Assessing daily intersections between transit accessibility and social disparity across the Greater Toronto and Hamilton Area (GTHA)

Public transportation systems generate economic benefits that can potentially reduce social disparities between populations when such benefits are distributed evenly within a region. However, the achievement of equity in the allocation of public resources is not easy to accomplish for land use and transportation planning agencies. This research seeks to determine whether people residing in socially disadvantaged areas in the Greater Toronto and Hamilton Area (GTHA), Canada, experience the same levels of transit accessibility as those living in other areas over the course of a day. Comparisons are presented in terms of regional accessibility, trends by social decile, spatial distribution of accessibility during the day, and travel time impacts. Findings suggest that residents in socially disadvantaged areas have equitable if not better transit accessibility to jobs than socially advantaged groups, and this is reflected in shorter travel times. However, the degree and impact of this advantage varies over the course of the day. Findings from this research can be of interest to transportation planners, engineers, and policy makers as it highlights deficiencies with current equity assessment practices that do not take into account variation in transit services over a 24-h time period.

Transitory Optimism: Changes in Passenger Perception Following Bus Service Improvement over Time

Passengers’ perception and satisfaction have long been seen and used as important measures of transit service quality and attractiveness. This research tried to understand better transit passengers’ perception of the implementation of various improvement strategies in bus service over time. The study analyzed three surveys of bus user perceptions conducted over a period of 3 years. The study also used stop-level data collected from the Société de Transport de Montréals automated vehicle location and automatic passenger count systems and bus schedules in Montreal, Canada, to measure the actual changes in service. Descriptive statistics and regression models were used for a better understanding of the differences between perceptions and reality. The implementation of various strategies had a limited impact on the short-term overestimation by users of their waiting time benefits, whereas the implementation had a long-term impact on their travel time overestimation. This study can be of interest to marketing and planning departments at transit agencies, because it provides them with new insights into passengers’ perception and satisfaction.

The Farside Story Measuring the Benefits of Bus Stop Location on Transit Performance

Determining the proper location of bus stops is an important planning decision in the transit planning field. While previous efforts in the literature have suggested several advantages and disadvantages of certain bus stop placements, there has been little effort toward understanding the impacts of bus stop location on the transit system performance at the stop level of analysis. This paper evaluates the impact of bus stop location on bus stop time and stop time variation. The paper uses stop level data collected from the Societe de Transport de Montreal’s automatic vehicle location and automatic passenger counting systems in Montreal, Quebec, Canada. The study findings show that stop times occurring on the nearside of intersections are on average 4.2 to 5.0 s slower than stop times occurring on the farside of intersections, with no impact on stop time variation. A validation model was used to confirm the impacts of bus stop placements on stop time with data from TriMet’s automated bus dispatch system in ...Determining the proper location of bus stops is an important planning decision in the transit planning field. While previous efforts in the literature have suggested several advantages and disadvantages of certain bus stop placements, there has been little effort toward understanding the impacts of bus stop location on the transit system performance at the stop level of analysis. This paper evaluates the impact of bus stop location on bus stop time and stop time variation. The paper uses stop level data collected from the Société de Transport de Montréals automatic vehicle location and automatic passenger counting systems in Montreal, Quebec, Canada. The study findings show that stop times occurring on the nearside of intersections are on average 4.2 to 5.0 s slower than stop times occurring on the farside of intersections, with no impact on stop time variation. A validation model was used to confirm the impacts of bus stop placements on stop time with data from TriMets automated bus dispatch system in Portland, Oregon. This study offers transit planners and policy makers a better understanding of the effects of bus stop location on stop time and its variation to improve service quality while minimizing service variation at the stop level.

Perspectives on Transit: Potential Benefits of Visualizing Transit Data

Advancements in information and communication technologies have enabled transit agencies around the world to generate streams of data on a high-frequency basis. Increasingly, these agencies are interested in new methods of visualizing these data to communicate the results of their planning efforts, operational investments, and overall transit performance to decision makers and stakeholders. Most agencies today collect and provide numerous kinds of data, including Google’s general transit feed specification schedule data, automatic vehicle location data, and automatic passenger count data. This paper aims to demonstrate the untapped potential of these data sources; specifically, the paper uses transit data from Montreal, Quebec, Canada, to generate performance measures that are of interest to both transit planners and marketing professionals. Some of these measures can also help in communicating the positive attributes of public transportation to the community. Performance measures are generated at different scales, including transit system, neighborhood, route, and stop levels. This paper expands on previous research on transit performance research and visualization by adopting currently available resources for so-called big data.