Assessing the Relationship between Access Travel Time Estimation and the Accessibility to High Speed Railway Station by Different Travel Modes

This paper aims to fill the research gap of the relationship between the access travel time (ATT) estimation and the accessibility to high speed railway (HSR) station. A regression analysis was developed on the basis of risk-return model to analyze the access travel time estimation error (ATTEE). The data sources were 1595 valid interview survey data at Beijing South Railway Station (BSRS), China in October 2016. The factors and scenarios included travel mode, departure time, and travel date, etc. The coefficients of ATT estimation were obtained by different travel modes. The results showed that the expected access travel time (EATT) has positive linear correlation with the actual access travel time (AATT). Accessibility was calculated by the ratio of AATT to EATT. The accessibility coefficients ranged from 0.89 to 1.38 in different travel modes, departure time, and travel dates. A smaller coefficient indicates better travel time reliability and accessibility. This study not only provides a useful tool to estimate the travel time budget required for access to HSR station, but also establishes a connection with the accessibility and ATTEE. It offers an opportunity to estimate ATT to HSR stations by different modes of transport, which can help to better understand how the accessibility of the feeder transport changes at different time periods.

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Micro-Simulation System on Passengers of the PDL Station Design

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.493 ◽

2011 ◽

Vol 467-469 ◽

pp. 493-496 ◽

Cited By ~ 1

Author(s):

Chun Xia Gao ◽

Bao Tian Dong ◽

Qian Li ◽

Ai Li Wang

Keyword(s):

High Speed ◽

Simulation System ◽

Railway Station ◽

High Speed Railway ◽

Micro Simulation ◽

Output Module ◽

The Relationship

The paper presents the context of the micro-simulation on passengers of high-speed railway station, designs the micro-simulation system, meanwhile, divides the system into four modules, they are: passengers setting module, station design module, passengers simulation module and results output module. The functions and detailed contents of the modules are described, at the same time, the models of each module involved are introduced, and the relationship between modules are represented.

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Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.776.80 ◽

2015 ◽

Vol 776 ◽

pp. 80-86

Author(s):

Amirotul M.H. Mahmudah ◽

A. Budiarto ◽

S.J. Legowo

Keyword(s):

Travel Time ◽

Travel Behavior ◽

Estimation Error ◽

Time Estimation ◽

Transportation Policy ◽

Time Slice ◽

Accurate Estimation ◽

Travel Time Estimation ◽

Traffic Condition ◽

On Line

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

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A new low complexity bus travel time estimator for fleet management system

10.21203/rs.3.rs-979905/v1 ◽

2021 ◽

Author(s):

Emad Alizade ◽

Ali Hendessi ◽

Faramarz Hendessi ◽

Massoud Reza Hashemi

Keyword(s):

Data Processing ◽

Travel Time ◽

Public Transportation ◽

Arrival Time ◽

Estimation Error ◽

Time Estimation ◽

Low Complexity ◽

Travel Time Estimation ◽

Mean Square ◽

Related Data

Abstract Improving the experience of using the public transportation system can be done by estimating the arrival time of the bus and notifying the passengers. Consequently, the accuracy of the estimation affects this experience. As the number of buses, stations, and service areas increases, so does the data collected in the cloud, making travel time estimation-related data processing more challenging. Despite this challenge, a distributed method for estimating the arrival time of the bus is considered in this paper. Also, we present a way to decentralize data processing and distribute it on each bus. Besides, using the Kalman filter and updating the estimated values at short intervals improves the estimation error. Examination of the degree of complexity shows that the proposed method has significantly reduced the complexity in the cloud, which makes the proposed method can be implemented in metropolitan areas. The results of implementation on a dataset, show that the proposed method has a good performance in terms of mean square error and root mean square.

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Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

International Journal of Modern Physics C ◽

10.1142/s0129183120500230 ◽

2019 ◽

Vol 31 (02) ◽

pp. 2050023

Author(s):

Sida Luo

Keyword(s):

Travel Time ◽

Traffic Congestion ◽

Temporal Distribution ◽

Time Estimation ◽

Wave Model ◽

Model Parameters ◽

Travel Time Estimation ◽

Time Model ◽

Departure Time ◽

Departure Time Choice

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

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Estimation of expected travel time using the method of moment

Canadian Journal of Civil Engineering ◽

10.1139/l10-115 ◽

2011 ◽

Vol 38 (2) ◽

pp. 154-165 ◽

Cited By ~ 13

Author(s):

Lu Sun ◽

Wenjun Gu ◽

Hani Mahmassani

Keyword(s):

Travel Time ◽

Time Estimation ◽

Random Variable ◽

Accurate Estimation ◽

Travel Time Estimation ◽

Method Of Moment ◽

Deterministic Function ◽

Wide Range ◽

Departure Time ◽

Ensemble Mean

Daily travel time is cast into a framework of nonstationary stochastic process. For a fixed value of departure time in a day, travel time given origin, destination, and route information, is treated as a random variable. For a specific date, travel time is treated as a deterministic function of departure time t. Under this framework, the expected travel time for a given departure time is defined as an ensemble mean travel time (EMTT) over a number of days. The method of moment is proposed to compute EMTT based on a hypothetical piecewise constant speed trajectory for travel time estimation. The advantage of the method of moment for EMTT estimation is that it only requires ensemble mean and ensemble variance of spot speed information at point detectors, which is much easier and cost-effective to get than obtaining collections of massive spot speed data per se. The result is compared against Monte Carlo simulation and direct sampling based simulation. The proposed method of moment approach provides accurate estimation of EMTT (e.g., the expected travel time estimation) under a wide range of traffic conditions (e.g., free flow and congestion).

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