Exploring Transit Performance And Traffic Congestion in Downtown Toronto Using Big Data

This exploratory research evaluates the linkages between roadway operations and mixed-traffic transit performance on three arterial corridors in Toronto- King Street, Queen Street, and Dundas Street. Using Inrix traffic speed probe data as well as GPS location data from the Toronto Transit Commission’s vehicles between January 2014 and June 2016, this research visualizes spatial and temporal trends in traffic congestion and transit headway regularity. Three regression models were estimated that indicate both traffic congestion and terminus departure times are statistically significant, but weak predictors of mixed-traffic transit reliability. These models leave most of the variability unexplained. The findings highlight opportunities and limitations for congestion management and transit scheduling as tools for improving headway reliability. They also illustrate the complexity of the relationships between transportation modes in downtown Toronto.

Exploring Transit Performance And Traffic Congestion in Downtown Toronto Using Big Data

This exploratory research evaluates the linkages between roadway operations and mixed-traffic transit performance on three arterial corridors in Toronto- King Street, Queen Street, and Dundas Street. Using Inrix traffic speed probe data as well as GPS location data from the Toronto Transit Commission’s vehicles between January 2014 and June 2016, this research visualizes spatial and temporal trends in traffic congestion and transit headway regularity. Three regression models were estimated that indicate both traffic congestion and terminus departure times are statistically significant, but weak predictors of mixed-traffic transit reliability. These models leave most of the variability unexplained. The findings highlight opportunities and limitations for congestion management and transit scheduling as tools for improving headway reliability. They also illustrate the complexity of the relationships between transportation modes in downtown Toronto.

Coordinated Headway-Based Control Method to Improve Public Transit Reliability considering Control Points Layout

The headway-based control method is usually used to regulate the bus headways and improve reliability of public transit. In general, the holding control strategy is applied at the control point, because enough space for dwell longer at the control point is required, while the stop-skipping control strategy can be used at any bus stop. However, in the headway-based control method, too much stop-skipping will bring longer waiting time and make the passengers impatient. The number and distribution of control points for stop-skipping are not considered in previous self-equalizing bus headway control works. Therefore, in this paper, the control points selection rules for stop-skipping involving their number and distribution on the bus route are discussed. A second by second discrete system is formulated to describe the bus operation. In the proposed control method, the threshold value for activating stop-skipping strategy is raised, avoiding provoking much additional waiting time because of boarding rejected. In the numerical analysis, a set of cases are conducted to evaluate the performance of control method under different number and distribution of control points for stop-skipping. The numerical results show that distribution of control points for stop-skipping has a greater influence on the public transit than the number.

Reliability prediction of further transit service based on support vector machine

The requirement for transit reliability grows with the increase of pace of life since unstable bus arrivals can raise the anxiety of waiting passengers. This paper proposes a reliability assessment method to evaluate the reliability of each bus stop on the route and the reliability of bus routes. In reliability prediction, the prediction target is locked by rolling horizon to reduce the interference of other information. In addition, a prediction method of the reliability of further transit service using the accurate online support vector machine is proposed. This prediction can provide more accurate and stable data for the arrival of buses and reduce unnecessary waiting of passengers. Finally, the reliability prediction method proposed is tested with the real data of a bus route in Dalian, China. The results show that the accurate online support vector machine with reasonable parameters can predict the reliability of transit service accurately.

Meaningful Modeling of Section Bus Running Times by Time Varying Mixture Distributions of Fixed Components

Understanding the variability of bus travel time is a key issue in the optimization of schedules, transit reliability, route choice analysis, and transit simulation. The statistical modeling of bus travel time data is of increasing importance given the increasing availability of data. In this paper, we introduce a novel approach to modeling the day-to-day variability of urban bus running times on a section level. First, the explanatory power of conventionally used distributions is examined, based on likelihood and effect size. We show that a mixture model is a powerful tool to increase fitting performance, but the applied components need to be justified. To overcome this issue, we propose a novel model consisting of two individual characteristic distributions representing either off-peak or peak hour dynamics. The observed running time distribution at every hour of the day can be described as a combination (mixture) of the two dynamics. The proposed time varying model uses a small set of parameters, which are physically interpretable and capable of accurately describing running time distributions. With our modeling approach, we reduce the complexity of mixture models and increase the explanatory power and fit compared with conventional models.

Traffic and Transit Travel Time Reliability Indexes and Confidence Intervals

As congestion worsens, the importance of rigorous methodologies to estimate travel time reliability increases. Exploiting fine-granularity transit GPS data, this research proposes a novel method to estimate travel time percentiles and confidence intervals. Novel transit reliability measures based on travel time percentiles are proposed to identify and rank low-performance hot spots; the proposed reliability measures can be utilized to distinguish peak-hour low performance from whole-day low performance. As a case study, the methodology is applied to a bus transit corridor in Portland, Oregon. Time–space speed profiles, heat maps, and visualizations are employed to highlight sections and intersections with high travel time variability and low transit performance. Segment and intersection travel time reliability are contrasted against analytical delay formulas at intersections—with positive results. If bus stop delays are removed, this methodology can also be applied to estimate regular traffic travel time variability.

Use of Automated Vehicle Location Data for Route- and Segment-Level Analyses of Bus Route Reliability and Speed

Reliability and speed are arguably the most important indicators of surface transit performance for both operators and passengers. They can be influenced by a variety of factors, including service characteristics of bus routes, physical infrastructure, signal settings, traffic conditions and ridership patterns. These factors have often been analyzed individually for their impact on transit reliability or speed. Studies considering more than one factor tend to use one or two transit routes to explore their effects. The study that is the subject of this paper proposed an evaluation framework to guide the selection of an appropriate reliability measure. Regression analysis was applied subsequently to determine the factors that exhibit a statistically significant relationship with transit reliability and speed at both the route and segment levels. Automated vehicle location data of a bus route sample that is representative of the entire bus network in the City of Toronto, Ontario, Canada were used. Features significantly associated with reliability and speed were compared. The results showed that lower transit reliability and speed are significantly associated with the increase in service distance, signalized intersection density, stop density, volume of boarding and alighting passengers, and traffic volume. By segregating bus route segments on the basis of the presence of transit signal priority, the results of the segment-level model demonstrated the beneficial impact of transit signal priority on improving transit reliability.