Have they bunched yet? An exploratory study of the impacts of bus bunching on dwell and running times

Abstract

If transit agencies wish to retain and attract riders, they need to provide reliable and efficient services. Transit agencies tend to run high-frequency bus routes during peak hours, and in many 3 cities, different routes can also overlap along major corridors. In some instances, consecutive buses can arrive at a shared stop simultaneously or one bus may arrive while another bus is currently servicing the stop. This phenomenon, known as bus bunching, can delay buses and 6 passengers, and is usually inefficient. In this study, we attempt to understand how bus bunching 7 from the same or different routes can impact bus operations, specifically dwell and running 8 times. This research uses stop-level records obtained from automatic vehicle location (AVL) and automatic passenger counter (APC) systems from TriMet, Portland, OR. Using linear modeling, we find that bus bunching increases both dwell and running times. Specifically, when different routes bunch or are scheduled to arrive at a bus stop within a short time frame, or when buses from the same route arrive with a short time frame, dwell times increase by ~10 s. Similarly, bus bunching from the same route or different route prolongs running times by ~40 s. Our findings suggest that bus schedulers and operators should consider adding more time between consecutive buses from different routes at shared stops to minimize the negative impacts that we observed from bus bunching.

Predictive Analytics of Streetcar Bunching Occurrence Time for Real-Time Applications

“…Similarly, operational data was used in (9) to understand bunching. Other researchers have focused on understanding the impact of bus bunching on service running time and dwell time (13). Also, (12) used AVL data at around 360 bus stops in Athens, Greece to investigate the factors affecting bus bunching at the network level, while considering their spatial variability.…”

Analysis and Simulation of Intervention Strategies against Bus Bunching by means of an Empirical Agent‐Based Model

“…e pervasiveness [1][2][3][4][5] of bus bunching has been a longstanding problem in public transportation systems. Bus bunching occurs when the distance between two or more buses reduces to near zero.…”

“…Bus bunching occurs when the distance between two or more buses reduces to near zero. In traffic studies [1][2][3][4][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], bus bunching is often examined through the headway between two buses, i.e., the forward distance or time of a vehicle to its leading vehicle. Bunching occurs naturally as a consequence of the interaction between buses and passengers [2,4,5] after discounting the effects of other traffic.…”

Optimal design for urban transit systems

“…The reliability of public transport services is one of the most important aspects of service quality (Verbich et al, 2016). Evaluation of service reliability is based on performance indicators, such as excess waiting time, headway standard deviation, service reliability index and headway adherence (Hickman, 2004).…”

“…When headway deviations become too large and corresponding headways very small, bus bunching typically occurs. Bus bunching events, during which consecutive buses run too close together and arrive simultaneously at the same stop (Rashidi et al, 2017;Verbich et al, 2016), result in poor service reliability. Increased waiting times, lower comfort in buses and inefficient capacity utilization are considered as the main impacts of bus bunching (Diab et al, 2015;Moreira-Matias et al, 2014).…”