Perspectives on Driver Preferences for Dynamic Route Guidance Systems

1997 ◽  
Vol 1588 (1) ◽  
pp. 26-31 ◽  
Author(s):  
Joseph L. Schofer ◽  
Frank S. Koppelman ◽  
William A. Charlton
Keyword(s):  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Traffic Information ◽  
Route Guidance ◽  
Real Time Traffic ◽  
Operational Test ◽  
Guidance Systems ◽  
Dynamic Route ◽  
And Storage

Insights about the design of route guidance systems based on the needs and desires of drivers who are familiar with the travel network are provided. Results from the ADVANCE Intelligent Transportation System operational test, in which more than 100 drivers used vehicles equipped with dynamic route guidance systems for 2-week periods, suggest that such drivers value real-time traffic information, and they want to incorporate their own knowledge and perspectives into the development of route plans, which they expect to be superior to those prepared by the navigation computer. This suggests that future route guidance systems likely to be targeted at familiar drivers should be based on a sharing of tasks between computer and driver that takes greater advantage of driver knowledge than that considered in current designs. Specifically, the driver should be able to take more responsibility for route planning, with the computer responsible mainly for traffic congestion data acquisition, organization and storage, and evaluation of driver-defined routes.

scholarly journals ROUTE PLANNING METHODOLOGY OF AN ADVANCED TRAVELLER INFORMATION SYSTEM IN VILNIUS CITY

Transport ◽  
2010 ◽  
Vol 25 (2) ◽  
pp. 171-177 ◽  
Author(s):  
Marius Jakimavičius ◽  
Marija Burinskienė
Keyword(s):  
Information System ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Service Providers ◽  
Information Service ◽  
Route Planning ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Driving Time ◽  
Planning Methodology

As a subsystem of an Intelligent Transportation System (ITS), an Advanced Traveller Information System (ATIS) disseminates real‐time traffic information to travellers. To help them with making better decisions on choosing their routes, a strong need to predict traffic congestion and to disseminate the predicted congestion information relating to travellers can be seen. This paper describes a methodology used by drivers for calculating an optimal driven route in Vilnius. The paper discusses how ATIS systems will likely evolve the experience of Information Service Providers (ISP) and optimal route planning calculations. A few methods of route planning have been taken into account. The paper presents the following types of route calculation: 1) the shortest route; 2) the quickest route; 3) the quickest forecasted route according to historical traffic information. Also, the paper deals with the architecture of the WEB based information system for drivers in Vilnius and analyzes data on traffic workflow. Furthermore, a comprehensive route planning procedure that forecasts data on driving time considering historical traffic is followed.

scholarly journals Traffic Management as a Service: The Traffic Flow Pattern Classification Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Carlos T. Calafate ◽  
David Soler ◽  
Juan-Carlos Cano ◽  
Pietro Manzoni
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Classification Problem ◽  
Time Dependent ◽  
Traffic Pattern ◽  
High Data ◽  
Real Time Traffic

Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To achieve such goal these systems should account for both estimated and real-time traffic congestion states, but obtaining reliable traffic congestion estimations for all the streets/avenues in a city for the different times of the day, for every day in a year, is a complex task. Modeling such a tremendous amount of data can be time-consuming and, additionally, centralized computation of optimal routes based on such time-dependencies has very high data processing requirements. In this paper we approach this problem through a heuristic to considerably reduce the modeling effort while maintaining the benefits of time-dependent traffic congestion modeling. In particular, we propose grouping streets by taking into account real traces describing the daily traffic pattern. The effectiveness of this heuristic is assessed for the city of Valencia, Spain, and the results obtained show that it is possible to reduce the required number of daily traffic flow patterns by a factor of 4210 while maintaining the essence of time-dependent modeling requirements.

scholarly journals Sistem Pengamatan Kondisi Lalu Lintas Berbasis Data GPS pada Smartphone (Studi Kasus : Kota Denpasar)

2020 ◽  
Vol 7 (4) ◽  
pp. 667
Author(s):  
Gede Herdian Setiawan ◽  
I Ketut Dedy Suryawan
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Data Retrieval ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Observation Model ◽  
Economic Activities ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Real Time Traffic

<p>Pertumbuhan jumlah kendaraan yang semakin meningkat setiap tahunnya mengakibatkan volume kendaraan yang melintasi ruas jalan semakin padat yang kerap mengakibatkan kemacetan lalu lintas. Kemacetan lalu lintas dapat menjadi beban biaya yang signifikan terhadap kegiatan ekonomi masyarakat. Informasi lalu lintas yang dinamis seperti informasi kondisi lalu lintas secara langsung <em>(real time)</em> akan membantu mempengaruhi aktivitas masyarakat pengguna lalu lintas untuk melakukan perencanaan dan penjadwalan aktivitas yang lebih baik. Penelitian ini mengusulkan model pengamatan kondisi lalu lintas berbasis data GPS pada <em>smartphone</em>, untuk informasi kondisi lalu lintas secara langsung. GPS <em>Receiver</em> pada <em>smartphone</em> menghasilkan data lokasi secara instan dan bersifat mobile sehingga dapat digunakan untuk pengambilan data kecepatan kendaraan secara langsung. Kecepatan kendaraan diperoleh berdasarkan jarak perpindahan koordinat kendaraan dalam satuan detik selanjutnya di konversi menjadi satuan kecepatan (km/jam) kemudian data kecepatan kendaraan di proses menjadi informasi kondisi lalu lintas. Secara menyeluruh model pengamatan berfokus pada tiga tahapan, yaitu akuisisi data kecepatan kendaraan berbasis GPS pada <em>smartphone</em>, pengiriman data kecepatan dan visualisasi kondisi lalu lintas berbasis GIS. Pengujian dilakukan pada ruas jalan kota Denpasar telah mampu mendapatkan data kecepatan kendaraan dan mampu menunjukkan kondisi lalu lintas secara langsung dengan empat kategori keadaan lalu lintas yaitu garis berwarna hitam menunjukkan lalu lintas macet dengan kecepatan kendaraan kurang dari 17 km/jam, merah menunjukkan padat dengan kecepatan kendaraan 17 km/jam sampai 27 km/jam, kuning menunjukkan sedang dengan kecepatan kendaraan 26 km/jam sampai 40 km/jam dan hijau menunjukkan lancar dengan kecepatan kendaraan diatas 40 km/jam.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The growth in the number of vehicles that is increasing every year has resulted in the volume of vehicles crossing the road increasingly congested which often results in traffic congestion. Traffic congestion can be a significant cost burden on economic activities. Dynamic traffic information such as information on real time traffic conditions will help influence the activities of the traffic user community to better plan and schedule activities. This study proposes a traffic condition observation model based on GPS data on smartphones, for information on real time traffic conditions. The GPS Receiver on the smartphone produces location and coordinate data instantly and is mobile so that it can be used for direct vehicle speed data retrieval. Vehicle speed is obtained based on the displacement distance of the vehicle's coordinates in units of seconds and then converted into units of speed (km / h), the vehicle speed data is then processed into information on traffic conditions. Overall, the observation model focuses on three stages, namely GPS-based vehicle speed data acquisition on smartphones, speed data delivery and visualization of GIS-based traffic conditions. Tests carried out on the Denpasar city road segment have been able to obtain vehicle speed data and are able to show traffic conditions directly with four categories of traffic conditions, namely black lines indicating traffic jammed with vehicle speeds of less than 17 km / h, red indicates heavy with speed vehicles 17 to 27 km / h, yellow indicates medium speed with vehicles 26 km/h to 40 km / h and green shows fluent with vehicle speeds above 40 km / h.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Travel Time Prediction on Un-Monitored Roads: A Spatial Factorization Machine Based Approach (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13855-13856 ◽  
Author(s):  
Lile Li ◽  
Wei Liu
Keyword(s):  
Traffic Congestion ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Time Of Arrival ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Large Cities ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Factorization Machine

Real-time traffic monitoring is one of the most important factors for route planning and estimated time of arrival (ETA). Many major roads in large cities are installed with live traffic monitoring systems, inferring the current traffic congestion status and ETAs to other locations. However, there are also many other roads, especially small roads and paths, that are not monitored. Yet, live traffic status on such un-monitored small roads can play a non-negligible role in personalized route planning and re-routing when road incident happens. How to estimate the traffic status on such un-monitored roads is thus a valuable problem to be addressed. In this paper, we propose a model called Spatial Factorization Machines (SFM) to address this problem. A major advantage of the SFM model is that it incorporates physical distances and structures of road networks into the estimation of traffic status on un-monitored roads. Our experiments on real world traffic data demonstrate that the SFM model significantly outperforms other existing models on ETA of un-monitored roads.

A New Data Aggregation Model for Intelligent Transportation System

2013 ◽  
Vol 671-674 ◽  
pp. 2855-2859
Author(s):  
Jun Wu ◽  
Luo Zhong
Keyword(s):  
Information System ◽  
Data Aggregation ◽  
Wireless Sensors ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
System Model ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Aggregation Problem

Intelligent Transportation System is a new kind of complicated information system which includes many different wireless sensors. With the development in sensor technologies and their applications, it is important to focus on how to find the useful and real-time traffic information from the Intelligent Transportation System. Using this method of building dynamical data system model for the Intelligent Transportation System is the way to solve the data aggregation problem and minimize the number of the multi-sources data.

scholarly journals Effect of Traffic Information on Travel Time of Medium-distance Trips: A Case Study in Tehran

2018 ◽  
Vol 30 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Roozbeh Mohammadi ◽  
Amir Golroo ◽  
Mahdieh Hasani
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Information Dissemination ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Trip Distance ◽  
Traffic Information Dissemination ◽  
Time Reduction

In populated cities with high traffic congestion, traffic information may play a key role in choosing the fastest route between origins and destinations, thus saving travel time. Several research studies investigated the effect of traffic information on travel time. However, little attention has been given to the effect of traffic information on travel time according to trip distance. This paper aims to investigate the relation between real-time traffic information dissemination and travel time reduction for medium-distance trips. To examine this relation, a methodology is applied to compare travel times of two types of vehicle, with and without traffic information, travelling between an origin and a destination employing probe vehicles. A real case study in the metropolitan city of Tehran, the capital of Iran, is applied to test the methodology. There is no significant statistical evidence to prove that traffic information would have a significant impact on travel time reduction in a medium-distance trip according to the case study.

Sign in / Sign up

Export Citation Format

Share Document