Travel time, speed, and delay analysis using an integrated GIS/GPS system

2002 ◽  
Vol 29 (2) ◽  
pp. 325-328 ◽  
Author(s):  
Ardeshir Faghri ◽  
Khaled Hamad
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Management ◽  
Time Data ◽  
Positioning Systems ◽  
Global Positioning ◽  
Comparative Statistical Analysis ◽  
Traffic Management System ◽  
Gps System ◽  
Transportation Studies

The backbone of any successful Integrated Traffic Management System (ITMS) for a metropolis is reliable, accurate, and real-time data. Travel time, speed, and delay are three of the most important factors used in ITMS for quantifying, monitoring, and controlling congestion. Global Positioning Systems (GPS) have recently become available for civil applications. As it provides real-time spatial and time measurements, it has an increasing use in conducting different transportation studies. This paper presents the application of GPS in collecting travel time, speed, and delay information on 64 major roads throughout the State of Delaware. A comparative statistical analysis was performed on data collected by GPS method, with data collected simultaneously by the conventional method. The GPS data proved to be at least as accurate as the data collected by conventional methods and was 50% more efficient in terms of manpower. Moreover, the sample-size requirement was determined to maintain 95% confidence level throughout the controlled test. Statistical trend analyses for the data collected from 1997 to 2000 are also presented and applications in the overall ITMS area are discussed.Key words: global positioning system, geographic information system, travel time and delay studies.

Development of an Intelligent Patrol Routing System Using GIS and Computer Simulations

2011 ◽  
pp. 339-351 ◽  
Author(s):  
Joseph Szakas ◽  
Christian Trefftz ◽  
Raul Ramirez ◽  
Eric Jefferis
Keyword(s):  
Information System ◽  
Real Time ◽  
Data Sets ◽  
Time Data ◽  
Map Algebra ◽  
Positioning Systems ◽  
Global Positioning ◽  
Real Time Data ◽  
Patrol Routing ◽  
Patrol Strategies

Patrolling in a nonrandom, but focused manner is an important activity in law enforcement. The use of geographic information systems, the emerging real-time data sets (spatial and nonspatial) and the ability via global positioning systems to identify locations of patrol units provide the environment to discuss the concept and requirements of an intelligent patrol routing system. This intelligent patrol routing system will combine available data utilizing Map Algebra and a data structure known as a Voronoi diagram to create a real-time updatable raster surface over the patrolling area to identify destination locations and routes for all patrol units. This information system will allow all patrol units to function “in concert” under a coordinated plan, and make good use of limited patrolling resources, and provide the means of evaluating current patrol strategies. This chapter discusses the algorithmic foundation, implications, requirements, and simulation of a GIS based intelligent patrol routing system.

Evaluation of Operational Effects of I-66 Active Traffic Management System

2017 ◽  
Vol 2616 (1) ◽  
pp. 91-103 ◽  
Author(s):  
PilJin Chun ◽  
Michael D. Fontaine
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Management System ◽  
Travel Time Reliability ◽  
Variable Speed ◽  
Time Data ◽  
Speed Limits ◽  
Variable Speed Limits ◽  
Traffic Management System ◽  
Virginia Department

In September 2015, the Virginia Department of Transportation instituted an active traffic management system on I-66 in Northern Virginia. I-66 is a major commuter route into Washington, D.C., that experiences significant recurring and nonrecurring congestion. The active traffic management system sought to manage existing capacity dynamically and more effectively with hard shoulder running, advisory variable speed limits, lane use control signs, and queue warning systems. An initial before-and-after analysis of the system’s operational effectiveness was performed with probe-based travel time data from the provider, INRIX, and used records from the active traffic management’s traffic operations center. On weekdays, statistically significant improvements were often observed during off-peak periods, but conditions did not improve during peak periods. Weekends showed the greatest improvements, with travel times and travel time reliability measures improving by 10% to 14%. Segment-level analysis revealed that most of the benefits were attained because of the use of hard shoulder running outside of the peak periods, which created additional capacity on I-66. Benefits due to advisory variable speed limits were inconclusive because of limited data.

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

Advanced Techniques for Travel Time Data Collection

1996 ◽  
Vol 1551 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Shawn M. Turner
Keyword(s):  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Cellular Phone ◽  
License Plate ◽  
Time Data ◽  
Video Imaging ◽  
Advantages And Disadvantages ◽  
Travel Time Data ◽  
Time Information

Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.

scholarly journals DYNAMIC TRIP PLANNER FOR PUBLIC TRANSPORT USING GENETIC ALGORITHM

Transport ◽  
2020 ◽  
Vol 35 (2) ◽  
pp. 156-167
Author(s):  
Abhishek Basu ◽  
Bharathi Raja ◽  
Rony Gracious ◽  
Lelitha Vanajakshi
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Public Transport ◽  
Public Transportation ◽  
Computation Time ◽  
Time Data ◽  
Transit System ◽  
Positioning Systems ◽  
Trip Planner ◽  
Chennai City

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles real-time Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem’s computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The “walk mode” and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner’s capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper.

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