Automated Bus Dispatching, Operations Control, and Service Reliability: Baseline Analysis

Strathman, James G.; Dueker, Kenneth J.; Kimpel, Thomas J.; Gerhart, Rick; Turner, Ken; Taylor, P B; Callas, Steve; Griffin, David; Hopper, Janet

doi:10.3141/1666-04

Cited by 114 publications

(78 citation statements)

References 8 publications

(14 reference statements)

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“…The archived AVL/APC data have been used in various studies of operations control and service reliability (Strathman et al 1999;Strathman et al 2000;Strathman et al 2001a;Strathman et al 2001b), for route-level passenger demand modeling (Kimpel 2001), for models of trip and dwell time (Bertini and El-Geneidy 2004), and for evaluating schedule efficiency and operator performance (Strathman, et al 2002).…”

Section: Computer-aided Dispatch (Cad) Centermentioning

confidence: 99%

Determinants of Bus Dwell Time

Dueker¹,

Kimpel²,

Strathman³

et al. 2004

JPT

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136

113

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Section: Computer-aided Dispatch (Cad) Centermentioning

confidence: 99%

Determinants of Bus Dwell Time

Dueker¹,

Kimpel²,

Strathman³

et al. 2004

JPT

Self Cite

136

113

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“…Using the travel-time estimation, average commercial speed of the buses was also estimated 8 . After the travel-time estimation and prediction studies, the travel-time variability (TTV) and reliability studies were reported 5,[9][10][11][12] . Day-to-day and period-toperiod travel-time reliability analysis was also carried out earlier 13 .…”

Section: Literature Reviewmentioning

confidence: 99%

Route Performance Evaluation of a Closed Bus Rapid Transit System Using GPS Data

Kathuria¹,

Parida²,

Sekhar³

2017

Current Science

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GPS-fitted buses operating in bus rapid transit systems (BRTS) ofKeywords: Bus rapid transit systems, GPS data, route performance, statistical parameters.PUBLIC transportation systems like bus rapid transit system (BRTS) are becoming popular all over the world because of their strong identity and low initial investment 1,2 . Satiennnam et al. 3 reported that BRTS has the capability of bringing a significant modal shift from private vehicles. BRTS are running in eight cities of India 4 ; buses running in these systems are mostly GPS-fitted which helps in collecting a wealth of travel-time data, but the methodologies to use the same for performance evaluation and system monitoring are limited. This article evaluates the performance of BRTS using the aforesaid GPS data for two routes in Ahmedabad. Both the selected routes operate from the innermost part of the city and then extend to the outer areas. These routes are majorly segregated from the normal traffic, but wherever sufficient right of way was not available, the buses were running with mixed traffic. It is thus important to evaluate the performance of such routes and carry out a segmentlevel analysis. Further, we have compared travel-time reliability of a BRTS and a non-BRTS route having similar characteristics in terms of land use and right of way. This was done to show the advantage of BRTS corridor in terms of travel-time reliability. Finally, after route evaluation we present a travel-time reliability and stabilitybased network level of service analysis. This was based on two indices, i.e. how the level of service had changed from 2013 to 2016 with the changing corridor length from 61 to 89 km. Literature reviewStudies in the past have reported using GPS data for various analyses in a bus transit system. The initial studies used GPS data to estimate travel time from the bus location data 5 . After developing techniques for travel-time estimation from GPS data, historical data were utilized for predicting travel time using artificial neural network and Kalman filter method 6,7 . Using the travel-time estimation, average commercial speed of the buses was also estimated 8 . After the travel-time estimation and prediction studies, the travel-time variability (TTV) and reliability studies were reported 5,9-12 . Day-to-day and period-toperiod travel-time reliability analysis was also carried out earlier 13 . GPS data were used to develop transit level of service based on the criterion of 'on time performance', in which the percentage of vehicles not arriving on time was computed by considering a vehicle to be on time if it was not more than 5 min late or 3 min early 14 . In another study, a new form of level of service (LOS) criterion was suggested based upon the weighted delay and acted as improvement over the conventional Transit Capacity and Quality Service Manual (TCQSM) 14 level of service ranges 15 . GPS data were also used to evaluate bus priority system; for which they were fed into a simulation software for carrying out sensitivity analysis 16 .

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“…There are a number of studies that have analysed bus TTV based on empirical data (e.g., Abkowitz and Engelstein, 1983;Strathman and Hopper, 1993;Strathman et al, 1999;El-Geneidy et al, 2008;Mazloumi et al, 2010;Moghaddam et al, 2011). Common indicators proposed to assess the reliability of a public transport service include the standard deviation of travel time, the probability of on-time performance 2 , the travel time ratio (observed travel time/scheduled travel time), the average additional travel time per passenger (Van Oort, 2011) and measures to analyse the variability of headways.…”

Section: Determinants Of Travel Time Variability: the Case Of Public mentioning

confidence: 99%

Accounting for travel time variability in the optimal pricing of cars and buses

2014

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A number of studies have shown that in addition to the common influences on mode, route and time of day of travel choices such as travel time and cost, travel time variability plays an increasingly important role, especially in the presence of traffic congestion on roads and crowding on public transport. The dominant focus of modelling and implementation of optimal pricing that incorporates trip time variability has been in the context of road pricing for cars. The main objective of this paper is to introduce a non-trivial extension to the existing literature on optimal pricing in a multimodal setting, building in the role of travel time variability as a source of disutility for car and bus users. We estimate the effect of variability in travel time and bus headway on optimal prices (i.e., tolls for cars and fares for buses) and optimal bus capacity (i.e., frequencies and size) accounting for crowding on buses, under a social welfare maximisation framework. Travel time variability is included by adopting the well-known mean-variance model, using an empirical relationship between the mean and standard deviation of travel times. We illustrate our model with an application to a highly congested corridor with cars and buses as travel alternatives in Sydney, Australia. There are three main findings that have immediate policy implications: (i) including travel time variability results in higher optimal car tolls and substantial increases in toll revenue, while optimal bus fares remain almost unchanged; (ii) when bus headways are variable, the inclusion of travel time variability as a source of disutility for users yields higher optimal bus frequencies; and (iii) including both travel time variability and crowding discomfort leads to higher optimal bus sizes. AcknowledgementsPart of this research was developed while the first author was with the Institute of Transport and Logistics Studies in Sydney. This study is supported in part by the Australian Research Council Discovery Program Grant DP120100201 titled: 'Valuation of Service Reliability and Crowding under Risk and Uncertainty: Neglected Drivers of Demand for Public Transport', and by the Complex Engineering Systems Institute, Chile (Grants ICM P-05-004-F, CONICYT FBO16). We thank NSW Roads and Maritime Services (RMS) for providing the data to estimate the travel time variability functions in Sydney, and the referees for their extensive and invaluable comments. 1 IntroductionTravellers do not like wasting time in traffic or waiting at a bus stop. A major element of transport research has focussed on estimating and monetising the (average) time savings of infrastructure investment and the demand management measures targeted at reducing travel times. We are, however, increasingly aware that users are not only willing to pay for a shorter travel time, but also for a more reliable trip. Uncertain travel times cause users to arrive earlier or later than expected at their destination, and influences mode choice, route choice and departure time decisions; suggest...

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Automated Bus Dispatching, Operations Control, and Service Reliability: Baseline Analysis

Cited by 114 publications

References 8 publications

Determinants of Bus Dwell Time

Determinants of Bus Dwell Time

Route Performance Evaluation of a Closed Bus Rapid Transit System Using GPS Data

Accounting for travel time variability in the optimal pricing of cars and buses

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