Travel time information assists road users in making informed travel decisions such as mode choice, route choice and/or time of travel. This study explores the use of GPS data from buses and Wi-Fi and Bluetooth data from a sample of vehicles, for accurate estimation of the travel time of all vehicles on the roadway. A 5.5 km road stretch in Chennai city was selected as study stretch and data were collected for a week’s period. The present study develops models using linear regression and artificial neural network (ANN) techniquesFto estimate stream travel time using bus travel time obtained from GPS. ANN performed better compared to the linear regression for all sizes of segments. Most of the Indian cities have an integrated network of buses traveling on most of the road segments with on-board tracking devices, making this a useful development for real-time travel time estimation. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Lelitha Devi Vanajakshi

Department Of Civil Engineering

Bluetooth

Bus transportation

Buses

Global positioning system

Linear Regression

Neural Networks

Roads and streets

Wi-Fi

Wireless local area networks (WLAN)

Accurate estimation

Integrated networks

PROBE VEHICLE DATA

REAL TIME TRAVEL

TRACKING DEVICES

TRAVEL DECISIONS

Travel time estimation

TRAVEL TIME INFORMATION

Travel time

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Reliable corridor level travel time estimation using probe vehicle data

Transportation Letters

Dynamic scheduling of buses, which adapts to the current passenger demand and traffic conditions, will help in ensuring an efficient service to the commuters and maximizing the profit for operators. A fixed schedule with a constant headway, which is currently used widely, may lead to inadequate number of buses during peak periods and under-utilization of the system in off-peak periods. To overcome this, the present study proposes a demand and travel time responsive model to maximize the benefit of the operator by preparing an optimal schedule that can adapt to the variations in passenger demands and traffic conditions, subjected to minimizing the waiting time of the passengers, capacity constraints of the buses to achieve the maximum financial benefit as well as social satisfaction. For this, the study analyses the data received from real-time tracking devices that were fitted in a selected bus route in Chennai. Results showed that the waiting times were reduced up to 10 min per passenger and the percentage utilization of bus capacity was increased by 8% on an average across a day. © 2019, Iran University of Science and Technology.

International Journal of Civil Engineering

Dynamic Bus Scheduling Based on Real-Time Demand and Travel Time

Travel time is one of the most preferred traffic information by a wide variety of travelers. Travel time information provided through variable message signs at the roadside could be viewed as a traffic management strategy designed to encourage drivers to take an alternate route. At the same time, it could also be viewed as a traveler information service designed to ensure that the driver has the best available information based on which they can make travel decisions. In an Intelligent Transportation Systems (ITS) context, both the Advanced Traveler Information Systems (ATIS) and the Advance Traffic Management Systems (ATMS) rely on accurate travel time prediction along arterials or freeways. In India, currently there is no permanent system of active test vehicles or license plate matching techniques to measure stream travel time in urban arterials. However, the public transit vehicles are being equipped with Global Positioning System (GPS) devices in major metropolitan cities of India for providing the bus arrival time information at bus stops. However, equipping private vehicles with GPS to enable the stream travel time measurement is difficult due to the requirement of public participation. The use of the GPS equipped buses as probe vehicles and estimating the stream travel time is a possible solution to this problem. The use of public transit as probes for travel time estimation offers advantages like frequent trips during peak hours, wide range network coverage, etc. However, the travel time characteristics of public transit buses are influenced by the transit characteristics like frequent acceleration, deceleration and stops due to bus stops besides their physical characteristics. Also, the sample size of public transit is less when compared to the total vehicle population. Thus mapping the bus travel time to stream travel time is a real challenge and this difficulty is more complex in traffic conditions like in India with its heterogeneity and lack of lane discipline. As a pilot study, a model based approach using the Kalman filtering technique to predict stream travel time from public transit is carried out in the present study. Since it is only a pilot study, only twowheeled vehicles have been considered as they constitute a major proportion in the study area. The prediction scheme is corroborated using field data collected by carrying GPS units in two-wheelers traveling along with the buses under consideration. The travel time estimates from the model were compared with the manually observed travel times and the results are encouraging. © 2011 IEEE.

IEEE Intelligent Vehicles Symposium, Proceedings

A model based approach to predict stream travel time using public transit as probes

Travel Time Observations Using Bluetooth MAC Address Matching: A Case Study on the Rajiv Gandhi Roadway: Chennai, India

Travel time and its reliability are becoming increasingly important for a variety of real time applications such as Advanced Traveller Information Systems (ATIS), Route Guidance Systems (RGS) etc. which are a part of the Intelligent Transport Systems (ITS). They assist the road users in making timely decisions regarding their trip such as departure time, mode choice, route choice etc. There are various sensors which capture and monitor the travel time across a study corridor. However, their performance under Indian conditions is a major concern due to the vast heterogeneity in vehicle classes and the lack of lane discipline. Data from the GPS units equipped in the public transport buses are the current source of reliable travel time information. But these buses account for only 1% of the total traffic and have an inherent modal bias. One such technology which can capture data from across the vehicle classes and from any roadway of interest is the Bluetooth sensor. This study reports on the use of a Bluetooth based sensor to capture the travel time data and evaluate the reliability along two alternate routes in Chennai, India. Results from the cumulative frequency diagrams (CFD's) which are used to evaluate the reliability are also compared against the various reliability index measures. © 2016 The Authors.

Fulltext

Transportation Research Procedia

Investigation of the Use of Bluetooth Sensors for Travel Time Studies under Indian Conditions

Transportation Research Record: Journal of the Transportation Research Board

Perception of Mode-Specific Travel Time Reliability and Crowding in Multimodal Trips

Impact of Parking Prices and Transit Fares on Mode Choice at the University of California, Berkeley

Journal	Data powered by TypesetTransportation Letters
Publisher	Data powered by TypesetTaylor and Francis Ltd.
ISSN	19427867
Open Access	No