Experimental Study of Real-Time Bus Arrival Time Prediction with GPS Data

Lin, Wei Hua; Zeng, Jian

doi:10.3141/1666-12

Cited by 136 publications

(53 citation statements)

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“…Broach et al, 2012) and bus travel (e.g. Lin and Zeng, 1999;Mazloumi et al, 2010), we are not aware of any 5 The predominance of SP data is generally true for the literature on the valuation of travel attributes. Among those studies that are (at least partially) based on RP data are Lam and Small (2001), Brownstone and Small (2005) and Börjesson (2008).…”

Section: Introductionmentioning

confidence: 99%

Train Commuters' Scheduling Preferences: Evidence from a Large-Scale Peak Avoidance Experiment

Peer

Knockaert²,

Verhoef

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe study the trip scheduling preferences of train commuters in a real-life setting. The underlying data have been collected during large-scale peak avoidance experiment conducted in the Netherlands, in which participants could earn monetary rewards for traveling outside peak hours. The experiment included ca. 1000 participants and lasted for about 6 months. Holders of an annual train pass were invited to join the experiment, and a customized smartphone app was used to measure the travel behavior of the participants. We find that compared to the pre-measurement, the relative share of peak trips decreased by 22% during the reward period, and by 10% during the post-measurement. By combining multiple complementary data sources, we are able to specify and estimate (MNL and panel latent class) departure time choice models. These yield plausible estimates for the monetary values that participants attach to reducing travel time, schedule delays, the number of transfers, crowdedness, and unreliability.

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Section: Introductionmentioning

confidence: 99%

Train Commuters' Scheduling Preferences: Evidence from a Large-Scale Peak Avoidance Experiment

Peer

Knockaert²,

Verhoef

2015

SSRN Journal

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“…Lin et al [7] suggested an arrangement of transport arrival time forecast calculations for a travel information system framework executed in Blacksburg, Virginia. Four calculations were presented with various assumptions on input information and were perform better than a few calculations from the literature.…”

Section: Related Workmentioning

confidence: 99%

Bus Location and Route System Using Internet of Things

Ruikar

Viswanathan

2017

Asian J Pharm Clin Res

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The fundamental point of undertaking is to plan a remote correspondence based transport position checking framework which is much helpful in operation. It is imperative to diminish traveler holding up time at transport stop, when clean time dining areas are obscure to traveler or traveler new in the city. To conquer the issue of travelers, the transport area and course route framework have the capacity effortlessly recover data about transport. Global positioning system, additionally furnishing clients with the most limited strolling course to closest bus stop.

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“…As implied earlier, most of the models found in the literature (e.g., Kalaputapu and Demetsky 1995;Lin and Zeng 1999;Wall and Dailey 1999;Farhan et al 2002) have included bus dwell times along any link in the travel time of that link (i.e., link travel time includes running plus dwell times). As such, these models cannot consider explicitly the effect of late or early bus arrivals at bus stops on the dwell times at those stops and vice versa.…”

Section: Approachmentioning

confidence: 99%

“…Several researchers (Kalaputapu and Demetsky 1995;Lin and Zeng 1999;Wall and Dailey 1999) have used AVL (and less often APC) data to develop models specifically for bus travel time prediction. The motivation for developing these models was mostly for providing information to transit riders on expected bus arrival times with virtually no sensitivity of such models to operations control strategies.…”

Section: Introductionmentioning

confidence: 99%