Description

Over the years, public transit agencies have been trying to improve their operations by continuously evaluating best practices to better serve patrons. Washington Metropolitan Area Transit Authority (WMATA) oversees the transit bus operations in the Washington Metropolitan Area (District of Columbia, some parts of Maryland and Virginia). One practice attempted by WMATA to improve bus travel time and transit reliability has been the implementation of designated bus lanes (DBLs). The District Department of Transportation (DDOT) implemented a bus priority program on selected corridors in the District of Columbia leading to the installation of red-painted DBLs on corridors of H Street, NW, and I Street, NW. This study evaluates the impacts on the performance of transit buses along with the general traffic performance at intersections on corridors with DBLs installed in Washington, DC by using a “before” and “after” approach. The team utilized non-intrusive video data to perform vehicular turning movement counts to assess the traffic flow and delays (measures of effectiveness) with a traffic simulation software. Furthermore, the team analyzed the Automatic Vehicle Locator (AVL) data provided by WMATA for buses operating on the study segments to evaluate bus travel time. The statistical analysis showed that the vehicles traveling on H Street and I Street (NW) experienced significantly lower delays during both AM (7:00–9:30 AM) and PM (4:00–6:30 PM) peak hours after the installation of bus lanes. The approximation error metrics (normalized squared errors) for the testing dataset was 0.97, indicating that the model was predicting bus travel times based on unknown data with great accuracy. WMATA can apply this research to other segments with busy bus schedules and multiple routes to evaluate the need for DBLs. Neural network models can also be used to approximate bus travel times on segments by simulating scenarios with DBLs to obtain accurate bus travel times. Such implementation could not only improve WMATA’s bus service and reliability but also alleviate general traffic delays.

Publication Date

6-2022

Publication Type

Report

Topic

Transit and Passenger Rail

Digital Object Identifier

10.31979/mti.2022.2040

MTI Project

2040

Mineta Transportation Institute URL

https://transweb.sjsu.edu/research/2040-Impacts-Bus-Lanes

Keywords

Bus transit operations, Traffic simulation, Transportation corridors, Bus and high occupancy vehicle facilities, Neural Networks

Disciplines

Infrastructure | Transportation | Transportation Engineering

2040-RB-Arhin-Impacts-Bus-Lane.pdf (1081 kB)
Research Brief

2040-dataset.zip (130 kB)
Dataset

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