SURVEYING THE FACTORS AFFECTING TRAVEL TIME IN PUBLIC TRANSPORTATION SYSTEMS AND PREDICTION OF TRAVEL TIME, CASE STUDY: TEHRAN BUS TRANSPORTATION SYSTEM

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AMIN NASERI MOHAMMAD REZA; BARADARAN V.

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Journal Paper

Paper Information

Journal: JOURNAL OF TRANSPORTATION RESEARCH Year:2009 | Volume:6 | Issue:3 (20) Page(s): 219-232

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Title

SURVEYING THE FACTORS AFFECTING TRAVEL TIME IN PUBLIC TRANSPORTATION SYSTEMS AND PREDICTION OF TRAVEL TIME, CASE STUDY: TEHRAN BUS TRANSPORTATION SYSTEM

Author(s)

AMIN NASERI MOHAMMAD REZA | BARADARAN V. | Issue Writer Certificate

Keywords

TRAVEL TIMEQ3

PUBLIC TRANSPORTATIONQ3

TEHRAN BUS SYSTEMQ2

ARTIFICIAL NEURAL NETWORKQ2

Abstract

Public transportation systems such as bus and railway transportation play the main role in transferring passengers and reduce problems in large cities due to low costs of traveling, high flexibility and ability in passengers transferring.Increasing users' satisfaction and optimal usage of existing facilities without planning is impossible. Vehicle assignment to routes, preparing time tables of trips, monitoring and implementation of schedules are the most important activities in PUBLIC TRANSPORTATION planning.Common process of planning in PUBLIC TRANSPORTATION systems includes four steps: route design, generation timetables of trips, schedule of vehicles and schedule of crews.In this process, short term activities at the beginning of each month or optimistically, at the beginning of each day, based on historical data set are planned. Hence, this type of planning is referred to as static planning.In most transportation systems such as Tehran bus transportation system, static planning, in which plans are fixed in different months, is used. However, recently, Intelligent Transportation System (ITS) and Advanced Traffic Management System (ATMS) have been extended in PUBLIC TRANSPORTATION systems. These systems prepare proper data set from location and state of vehicles and traffic variables and convert static planning to dynamic planning.Time of traveling is the most important factor in both static and dynamic planning. PUBLIC TRANSPORTATION systems planning without any information about bus TRAVEL TIME between stations is impossible and lead to unreal plans.In addition, estimation of TRAVEL TIME can be used for managing passenger’s demand and traffic via information systems as well as planning the other PUBLIC TRANSPORTATION systems such as subway, taxi and so on.In the previous research works, probability distributions are used to estimate the bus TRAVEL TIME in short period of time. Continuous changes of factors over TRAVEL TIME such as changing in traffic conditions in a route affect on probability distribution. Hence, a fixed probability distribution with known parameters cannot be considered for time travel variable during a day.Development of proper forecasting models should be considered to estimate TRAVEL TIME in all time periods during a day precisely. In dynamic planning, one line data sets are available in addition to the historical data set available in static planning.Forecasting methods with different performances can be designed and implemented to estimate TRAVEL TIME of public vehicles in both static and dynamic planning approach.In this paper, proper variables are defined to measure the effect of time, traffic, and location factors on the TRAVEL TIME of public vehicles in a route. The effect of the mentioned variables is investigated on TRAVEL TIME of bus system in Tehran.Two forecasting models are designed based on the identified variables to estimate TRAVEL TIME of public vehicle between two successive stations in a route.First, a model based on historical data set and multiple linear regressions is designed in which the relationship between TRAVEL TIME variable and the variables affect it is described.A feed forward ARTIFICIAL NEURAL NETWORK is developed to estimate TRAVEL TIME based on one line and historical data set. This model can be used in dynamic planning approach.Correlation coefficient between real and predicted data in regression and ARTIFICIAL NEURAL NETWORK models are about 75 and 94 percent, respectively, based on the data set from Tehran PUBLIC TRANSPORTATION system.

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