Factors affecting running time on transit routes

Abkowitz, Mark; Engelstein, Israel

doi:10.1016/0191-2607(83)90064-x

Cited by 113 publications

(73 citation statements)

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“…przez Stermana i Schofera [23] oraz Turnquista [28]. Abkowitz i Engelstein [1,2] w swoich pracach badali czynniki wpływające na czas jazdy pojazdów na obsługiwanych trasach i metody utrzymania regularności usług przewozowych. Stwierdzili oni między innymi, iż średni czas przejazdu jest silnie uzależniony od długości trasy, wsiadających i wysiadających ludzi oraz sygnalizacji na skrzyżowaniach.…”

Section: Doi 101515/jok-2016-0021 Krzysztof Bojda Agnieszka Tubisunclassified

“…The early studies in this field were presented by Sterman and Schofer [23], Turnquist [28]. The papers of Abkowitz and Engelstein [1,2] provide the factors influencing the riding time of vehicles on particular routes and methods of maintaining even services. They stated, among other aspects, that the average travel time is strongly dependent on the length of the route, number of passengers getting on and off and traffic signals at intersections.…”

Section: The Problem Of Punctuality Measurement Of Transportation Sermentioning

confidence: 99%

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The selected aspects of transport services punctuality measurement rated by the carrier and passengers / Wybrane aspekty pomiaru punktualności przewozów przez przewoźnika i pasażerów

Bojda

Tubis

2016

Journal of KONBiN

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Section: Doi 101515/jok-2016-0021 Krzysztof Bojda Agnieszka Tubisunclassified

Section: The Problem Of Punctuality Measurement Of Transportation Sermentioning

confidence: 99%

The selected aspects of transport services punctuality measurement rated by the carrier and passengers / Wybrane aspekty pomiaru punktualności przewozów przez przewoźnika i pasażerów

Bojda

Tubis

2016

Journal of KONBiN

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“…Service reliability, which is a result of variability in operations (Abkowitz and Engelstein 1983;Ceder 2007;Mazloumi et al 2010), is the certainty of service aspects (such as travel time) compared to the schedule as perceived by the user (Furth and Muller 2009;Van Oort et al 2012). Unreliability causes longer and uncertain passenger journeys (Bates et al 2001;Noland and Small 1995;Van Oort et al 2012).…”

Section: Passenger Impactsmentioning

confidence: 99%

Data driven improvements in public transport: the Dutch example

et al. 2015

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Due to reduced budgets, higher political expectations and increasing competition between operators, there is growing pressure on public transport companies and authorities to improve their operational efficiency. It is thus of utter importance for them to be able to identify inefficiencies, bottlenecks and potentials in their public transport service. Recorded operational data, which has quickly become more widespread in the last decade, aids greatly in this process and enables operators and authorities to continually improve. In this paper we identify some of the arising possibilities. We first describe the state of publicly available transit data, with an emphasis on the Dutch situation. Next, the value of insights from Automatic Vehicle Location data is demonstrated by examples. Thereafter, a software tool is presented that enables operators and authorities to quickly perform comprehensive operational analyses, and which was able to identify several bottlenecks when applied in practice.

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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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Factors affecting running time on transit routes

Cited by 113 publications

References 1 publication

The selected aspects of transport services punctuality measurement rated by the carrier and passengers / Wybrane aspekty pomiaru punktualności przewozów przez przewoźnika i pasażerów

The selected aspects of transport services punctuality measurement rated by the carrier and passengers / Wybrane aspekty pomiaru punktualności przewozów przez przewoźnika i pasażerów

Data driven improvements in public transport: the Dutch example

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

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