A model of bus bunching under reliability-based passenger arrival patterns.

If bus service departure times are not completely unknown to the passengers, non-uniform passenger arrival patterns can be expected. We propose that passengers decide their arrival time at stops based on a continuous logit model that considers the risk of missing services. Expected passenger waiting times are derived in a bus system that allows also for overtaking between bus services. We then propose an algorithm to derive the dwell time of subsequent buses serving a stop in order to illustrate when bus bunching might occur. We show that non-uniform arrival patterns can significantly influence the bus bunching process. With case studies we find that, even without exogenous delay, bunching can arise when the boarding rate is insufficient given the level of overall demand. Further, in case of exogenous delay, non-uniform arrivals can either worsen or improve the bunching conditions, depending on the level of delay. We conclude that therefore such effects should be considered when service control measures are discussed

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers

Implementation of Bus Rapid Transit in Copenhagen: A Mesoscopic Model Approach

Bus Rapid Transit (BRT) has shown to be an efficient and cost-effective mode of public transport, and has gained popularity in many cities around the world. To optimise the operations and infrastructure it is advantageous to deploy transport models. However, microscopic models are very inefficient for large scale corridors due to the vast amount of data and resources required. Hence, it is relevant to investigate how to model and evaluate BRT efficiently. In this paper the effects of implementing BRT in Copenhagen is discussed including how to evaluate and model bus operations. For this purpose, a mesoscopic simulation model is developed. In the model bus operations are modelled on a microscopic level whereas the interactions with other traffic are modelled macroscopically. This makes it possible to model high-frequency bus services such as BRT lines in more details without the time consumption of micro-simulation models. The developed model is capable of modelling bus operations in terms of travel time and reliability including important mode-specific issues such as bus bunching. The model is applied to a BRT project proposal with different combinations of BRT elements. The model results show that infrastructure upgrades (busways and enhanced stations) ensure a reduction to travel time whereas no improvements to reliability occur. Upgrades to technology and service planning (pre-paid fare collection, boarding and alighting from all doors, special BRT vehicles, ITS, and active bus control) ensure an increase in service reliability whereas only small reductions to travel time are observed. By combining all BRT elements it is possible to obtain synergies where the improved reliability due to planning and technology elements makes it possible to utilise the infrastructure optimally. Hence, it is possible to increase commercial speed from 14.8 to 19.9 km/h and service reliability in terms of headway time regularity from 46% to 84% aggregated on both directions for the morning peak period making the implementation of BRT feasible from a pure financial point of view

Analysing improvements to on-street public transport systems: a mesoscopic model approach

Light rail transit and bus rapid transit have shown to be efficient and cost-effective in improving public transport systems in cities around the world. As these systems comprise various elements, which can be tailored to any given setting, e.g. pre-board fare-collection, holding strategies and other advanced public transport systems (APTS), the attractiveness of such systems depends heavily on their implementation. In the early planning stage it is advantageous to deploy simple and transparent models to evaluate possible ways of implementation. For this purpose, the present study develops a mesoscopic model which makes it possible to evaluate public transport operations in details, including dwell times, intelligent traffic signal timings and holding strategies while modelling impacts from other traffic using statistical distributional data thereby ensuring simplicity in use and fast computational times. This makes it appropriate for analysing the impacts of improvements to public transport operations, individually or in combination, in early planning stages. The paper presents a joint measure of reliability for such evaluations based on passengers’ perceived travel time by considering headway time regularity and running time variability, i.e. taking into account waiting time and in-vehicle time. The approach was applied on a case study by assessing the effects of implementing segregated infrastructure and APTS elements, individually and in combination. The results showed that the reliability of on-street public transport operations mainly depends on APTS elements, and especially holding strategies, whereas pure infrastructure improvements induced travel time reductions. The results further suggested that synergy effects can be obtained by planning on-street public transport coherently in terms of reduced travel times and increased reliability

Increasing Boarding Lost Time at Regular Bus Stops during Rainy Conditions: A Case Study

[Abstract] Inclement weather conditions affect the behavior of travelers and transportation system operations. Understanding this influence can help improve operational planning schemes for any mode of transport, especially for buses. One of the factors that can be affected by rainfall is the time expended by a passenger from the moment a bus opens its doors until he/she boards the bus. This time is known as “boarding lost time” (BLT), and it was first introduced in the latest edition of the Transit Capacity and Quality of Service Manual (TCQSM). TCQSM only considers BLT for bus rapid transit (BRT) stations with more than one berth. Weather conditions are not considered when calculating the current BLT for a BRT system, given the provision of protective shelters over the entire boarding area of such stations. Furthermore, recommendations with regard to BLT for regular bus stops have not been provided. This paper presents analyses of BLT for a two-berth regular bus stop under different rainy conditions. The findings demonstrate that the increment in BLT between Berth 2 and Berth 1 under heavy rainfall is significantly higher than in the absence of rainfall.This study is part of the research project “Experimental analysis and modeling of the influence of stops, transfers, and right-of-way in bus systems,” reference RTI2018-097924-B-I00 MCIU/AEI/FEDER, UE

Empirical Analysis of Bus Bunching Characteristics Based on Bus AVL/APC Data

Bus bunching takes place when headways between buses are irregular. Bus bunching is associated with longer waiting times for riders, overcrowding in some buses, and an overall decrease on the level of service and capacity. Understanding the temporal and spatial characteristics and the causes and effects of bus bunching incidents from archived bus data can greatly aid transit agencies to develop efficient mitigation strategies. This paper presents methods to identify and visualize specific time periods and segments where bus bunching incidents occur based on automatic vehicle location (AVL) and automatic passenger count (APC) data. The paper also proposes methods that help analyze the causes and effects of bus bunching based on AVL/APC data. Temporal and spatial distributions of bus bunching events indicate high concentration during high frequency service hours and segments, and increasing concentration toward downstream. Time point bus stops can help reduce bus headway variability but with limited capability. Results indicate that irregular departure headway at the initial stop is the key cause of bus bunching rather than downstream traffic conditions and passenger demand uncertainty. A bus departure headway control at the initial stop of high frequency service zone is highly recommended, and a switch from schedule-based control to headway-based control strategy at time point stops in high frequency service zone is suggested

Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name “high passenger load”, “whole route”, “evening, end of route”, “long duration”. We analyse each bunching swings formation type in detail

Exploring Data Driven Models of Transit Travel Time and Delay

Transit travel time and operating speed influence service attractiveness, operating cost, system efficiency and sustainability. The Tri-County Metropolitan Transportation District of Oregon (TriMet) provides public transportation service in the tri-county Portland metropolitan area. TriMet was one of the first transit agencies to implement a Bus Dispatch System (BDS) as a part of its overall service control and management system. TriMet has had the foresight to fully archive the BDS automatic vehicle location and automatic passenger count data for all bus trips at the stop level since 1997. More recently, the BDS system was upgraded to provide stop-level data plus 5-second resolution bus positions between stops. Rather than relying on prediction tools to determine bus trajectories (including stops and delays) between stops, the higher resolution data presents actual bus positions along each trip. Bus travel speeds and intersection signal/queuing delays may be determined using this newer information. This thesis examines the potential applications of higher resolution transit operations data for a bus route in Portland, Oregon, TriMet Route 14. BDS and 5-second resolution data from all trips during the month of October 2014 are used to determine the impacts and evaluate candidate trip time models. Comparisons are drawn between models and some conclusions are drawn regarding the utility of the higher resolution transit data. In previous research inter-stop models were developed based on the use of average or maximum speed between stops. We know that this does not represent realistic conditions of stopping at a signal/crosswalk or traffic congestion along the link. A new inter-stop trip time model is developed using the 5-second resolution data to determine the number of signals encountered by the bus along the route. The variability in inter-stop time is likely due to the effect of the delay superimposed by signals encountered. This newly developed model resulted in statistically significant results. This type of information is important to transit agencies looking to improve bus running times and reliability. These results, the benefits of archiving higher resolution data to understand bus movement between stops, and future research opportunities are also discussed

Unsupervised approach towards analysing the public transport bunching swings formation phenomenon

We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques, namely clustering. We also show the correlation between bunching rate and passenger load, and bunching probability patterns for working days and weekends. We present the approach for extracting isolated bunching swings formations (BSF) and show different cases of BSFs, some of which can persist for a considerable time. We applied our approach to the tram line 1 of The Hague, and computed and presented four different patterns of BSFs, which we name “high passenger load”, “whole route”, “evening, end of route”, “long duration”. We analyse each bunching swings formation type in detail