Application of Dwell Time Functions in Transit Assignment Model

2002 ◽  
Vol 1817 (1) ◽  
pp. 88-92 ◽  
Author(s):  
Hedayat Z. Aashtiani ◽  
Hamid Iravani
Keyword(s):  
Transportation Planning ◽  
Dwell Time ◽  
Accurate Estimation ◽  
Model Output ◽  
Transportation Model ◽  
Transit Assignment ◽  
Assignment Model ◽  
Planning Software ◽  
In Transit ◽  
The City

The transit assignment process applied as part of the development of the Tehran transportation model is described. The process includes development of various models for dwell time as a function of transit volume. Dwell time is the time a transit vehicle spends at a stop to allow passengers to alight and board. This method was implemented by using EMME/2 transportation planning software. The calculation of dwell time is necessary in modeling transit assignment because an accurate estimation of dwell time will lead to more precise transit assignment results. The area analyzed in the model comprises various transportation analysis zones in the city of Tehran. The model output was shown to be statistically significant. Calculations were found to be valid when compared with observed data.

Napfmarathon versus Stadtmarathon: Eine Annäherung an das Konzept des Leistungskilometers

2015 ◽  
Vol 63 (1) ◽  
Keyword(s):  
Accurate Estimation ◽  
Horizontal Distance ◽  
First Approximation ◽  
The City

The aim of this study was to investigate differences in course times of a mountainmarathon (Napfmarathon) versus a city Marathon. Therefore all participants of Napfmarathon were screened concerning a double participation on a city marathon (Zürich, Winterthur, Lausanne, Luzern) and the course time were compared. Of key interest was the influence of ascents and descents which were quantified according to ­guidelines of Youth & Sport (Jugend + Sport / Jeunesse et Sport), whereby in first approximation 100 meter of ascent, 150 meter of descent (more than 20%) and 1 km of horizontal distance were taken as a simallar performance correlat. For the identified double starter different average times per km resulted. For the city marathon with an average time of 4 min 52 sec and for the Napfmarathon with 4 min 28 sec. If speed per km was calculated only with ascent and horizontal distances having performance relevance an average time of 4 min 56 sec per km was identified. This effect seems to be independet from distance absolved, resulting for Halbmarathon on an average time of distance of 4 min 13 sec, for Napfmarathon of 4 min 4 sec and for the performance concept only with ascent an average time per km of 4 min 16 sec. These analysis reveal, that if only ascent is taxed average course times differ less than 5 sec for both distances. For these particular reasons we recommend for running events to calculate only based on ascent and horizontal distances making necessary adjustments based on length of course, steepness of ascent and descent, character of terain (middle-country, pre-alps, alpes) for accurate estimation of course times.

Application of smart card data in validating a large-scale multi-modal transit assignment model

2017 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ahmad Tavassoli ◽  
Mahmoud Mesbah ◽  
Mark Hickman
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Transit Assignment ◽  
Assignment Model ◽  
Smart Card Data

Integrated simulation-based dynamic traffic and transit assignment model for large-scale network

2020 ◽  
Vol 47 (8) ◽  
pp. 898-907 ◽  
Author(s):  
Islam Kamel ◽  
Amer Shalaby ◽  
Baher Abdulhai
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic ◽  
Transit Assignment ◽  
Large Scale Network ◽  
Assignment Model ◽  
Morning Peak ◽  
Simulation Based ◽  
Scale Network ◽  
Assignment Models

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.

Sign in / Sign up

Export Citation Format

Share Document