For more than a decade, many urban transit systems have struggled to meet their service standards for both reliability and capacity provision. For example, San Francisco's Muni system is quite crowded, with official reports showing that over 5% of peak period travel happens on transit lines that are over 125% of their peak load capacity. The passenger experience is further degraded by the unevenness of the vehicle arrivals as only 6 out of 10 arrive within a few minutes of their scheduled headway . As a result, people are turned away from traveling on transit and on to congested roadways both because they cannot physically get on and because it can not reliably get them to work, appointments, or home.
While qualitative emphasis was placed on transit capacity and reliability for the adopted regional transportation plans in both the Bay Area and Puget Sound regions, no quantitative analysis of the repercussions of the "failure to act" were included because proper tools and data did not exist.
The historical emphasis of studying and planning-for vehicle-travel rather than person-travel has resulted in a multitude of heavily-researched and widely-accepted technical tools to support vehicle-travel, but few if any to analyze transit reliability and capacity. Following the adoption of the San Francisco Transportation Plan and Plan Bay Area, planners and policy makers applied for and won two significant grants to address this disparity ahead of the next round of regional plans. The Metropolitan Transportation Commission led two multi-agency collaborations:
Following various contracting and startup steps, we officially kicked off the project in winter 2015
The first major step is to complete background research on related methods and practices in order decide on the best methodological approach for our own work. At the same time, we will need to develop data standards to be used for our study, because existing standards such as GTFS do not include all of the fields and information that we need in order to capture relevant transit features.
Over the summer, the Fast-Trips software will be refactored form C++ to Python. Additionally, four pieces of data will be developed and refined:
In addition to using this time to improve the performance of the Fast-Trips transit simulator, we will be estimating route-choice models from observed transit route data using BIOGEME software in order to deduce how much transit passengers of various market segments (i.e. youth, commuters, elderly etc.) value various service features (time, reliability, seat availability, walking distance etc.) of each component (walk access, waiting, transfers, in-vehicle time) of their transit route. These parameters will be used within the Fast-Trips simulation.
The final step involves running the entire transit model system and making sure it is a sufficient representation of reality in terms of overall accuracy of forecasts, but more importantly that it exhibits appropriate sensitivity to service quality and capacity changes so that it can be used in evaluating planning and policy changes.