Modeling Incident-Related Routing Decisions by Using a Nested Logit Structure

1998 ◽  
Vol 1645 (1) ◽  
pp. 103-110 ◽  
Author(s):  
Mohamed A. Abdel-Aty
Keyword(s):  
Logit Model ◽  
Urban Areas ◽  
Potential Benefit ◽  
Traffic Information ◽  
Nested Logit ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Real Time Traffic ◽  
Alternative Routes ◽  
Logit Modeling

Unusual congestion that could be caused by an incident or other traffic problems is a major source of delay for drivers in urban areas. Real-time traffic information, the building block for advanced traveler information systems (ATIS), has a promising potential for alleviating such congestion by encouraging and assisting drivers to divert to less congested routes. Traffic information is envisioned to help more informed routing decisions in case of incident-related congestion. Drivers’ routing decisions made when they are faced with such unusual congestion are investigated. The factors that influence these decisions are explored, including the effect of traffic information. A nested logit modeling structure is introduced. This model proved that the nested logit approach is superior than the simple multinomial logit in modeling the choice in cases of incident-related congestion. The model also showed that the decisions not to divert from the usual route and to divert but only around the location of the problem share unobserved terms. Familiarity and usual use of alternative routes did not affect the decision in the case of an incident. Drivers who use more than one route to work do not necessarily switch routes if they encounter unusual congestion. The nested logit model also proved the significance of traffic information, indicating a promising potential benefit of ATIS in alleviating nonrecurring congestion.

Dynamic Aspects of Commuter Decisions Under Advanced Traveler Information Systems: Modeling Framework and Experimental Results

1998 ◽  
Vol 1645 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Yu-Hsin Liu ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
Switching Behavior ◽  
Traffic Information ◽  
Advanced Traveler Information Systems ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Real Time Traffic ◽  
Departure Time ◽  
Real Time Information ◽  
Time Information

Previous work on the effect of advanced traveler information systems was concerned primarily with immediate route choice decisions in response to real-time traffic information. Real-time traffic information also influences day-to-day decisions of trip makers, including departure time and route choices. Joint departure time decision and pretrip route selection are addressed, as well as en route path switching behavior by commuters under real-time information availability. Data were used from laboratory experiments using a dynamic interactive traveler simulator that allows actual commuters to simultaneously interact with each other within a simulated traffic corridor. Given real-time information provided by the system, commuters determine their departure time and route at the origin and select paths en route at various decision nodes along the trip. Day-to-day dynamic models of commuters’ joint departure time and route switching decisions are developed and calibrated by using a multinomial probit model framework that takes into account commuters’ learning from experience. The analysis provides insight into day-to-day effects of real-time traffic information on user decisions. Results indicate that the reliability of real-time information and supplied schedule delay (relative to the commuters’ preferred arrival time) are significant variables that influence users’ indifference band governing route switching behavior both pretrip and en route. These models are intended for use within evaluation frameworks (e.g., simulation-assignment models). In addition, the substantive insights provide guidelines for the design of real-time information content and systems.

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>

Driver Acceptance of Unreliable Route Guidance Information

1994 ◽  
Vol 38 (16) ◽  
pp. 1062-1066 ◽  
Author(s):  
Richard J. Hanowski ◽  
Susan C. Kantowitz ◽  
Barry H. Kantowitz
Keyword(s):  
Real Time ◽  
Heavy Traffic ◽  
Traffic Information ◽  
Route Guidance ◽  
Experimental Conditions ◽  
Traveler Information ◽  
Traveler Information Systems ◽  
Traffic Conditions ◽  
Driver Acceptance ◽  
Real Time Information

Human factors research can be used to design safe and efficient Advanced Traveler Information Systems (ATIS) that are easy to use (Kantowitz, Becker, & Barlow, 1993). This research used the Battelle Route Guidance Simulator (RGS) to examine two important issues related to driver behavior and acceptance of ATIS technology: (1) the effect of route familiarity on ATIS use and acceptance and (2) the level of information accuracy needed for an ATIS to be accepted and considered useful. The RGS included two 486 computers that provided drivers with real-time information and traffic reports. Drivers used a touch screen to select routes on one computer monitor and watched the results of their selection (i.e., real-time video of the traffic) on a second computer monitor. Drivers could use the system to obtain information about the traffic conditions on any link before traversing a route. In this experiment, subjects were exposed to four experimental conditions involving manipulation of the driver's familiarity with the route and the reliability of the traffic information obtained from the RGS (i.e., 100%, 71%, and 43% accuracy). The driver's goal was to reach the destination as quickly as possible by avoiding heavy traffic. The results indicated that drivers were able to benefit from system information when it was reliable, but not when it was unreliable. Trust ratings for the 43% accuracy group were significantly higher at the beginning of the four trials than at the end. Also, drivers were more apt to rely on the ATIS and accept information given in an unfamiliar traffic network versus a familiar one.

Dynamic Model Research of Departure Time and Route Choice Based on Prospect Theory

2011 ◽  
Vol 243-249 ◽  
pp. 4418-4421
Author(s):  
Zhi Yong Yang ◽  
Gui Yun Yan
Keyword(s):  
Prospect Theory ◽  
Choice Model ◽  
Weighting Function ◽  
Route Choice ◽  
Traffic Information ◽  
Traveler Information ◽  
Travel Choice ◽  
Traveler Information Systems ◽  
Model Research ◽  
Departure Time

This paper takes commuters’ daily travel as research object to build model of travel choice which contains departure time and travel route based on Prospect Theory. Choosing the time of arriving destination as reference point, commuter will choose the time at which he/she can obtain the maximum value as departure time, then establishes choice model of departure time. Using Bayesian Theory to update and adjust route’s forecasting travel time in light of traffic information provided by Advanced Traveler Information Systems (ATIS) and travelers’ previous experience information. Gets decision weighting function after having analyzed traveler’s individual subjective probability which is about the possible result for route choice, then obtains the expression of travel route’s prospect value and gets route choice model. Finally, by designing a network to analyze the dynamic choice model, and achieves expected effect.

scholarly journals Methodology for Calculating Latency of GPS Probe Data

2017 ◽  
Vol 2645 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Zhongxiang Wang ◽  
Masoud Hamedi ◽  
Stanley Young
Keyword(s):  
Real Time ◽  
Ground Truth ◽  
Transportation Systems ◽  
Traffic Information ◽  
Reference Source ◽  
Traveler Information ◽  
Ground Truth Data ◽  
Real Time Traffic ◽  
Message Signs ◽  
Road Segmentation

Crowdsourced GPS probe data, such as travel time on changeable-message signs and incident detection, have been gaining popularity in recent years as a source for real-time traffic information to driver operations and transportation systems management and operations. Efforts have been made to evaluate the quality of such data from different perspectives. Although such crowdsourced data are already in widespread use in many states, particularly the high traffic areas on the Eastern seaboard, concerns about latency—the time between traffic being perturbed as a result of an incident and reflection of the disturbance in the outsourced data feed—have escalated in importance. Latency is critical for the accuracy of real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring probe data latency regarding a selected reference source. Although Bluetooth reidentification data are used as the reference source, the methodology can be applied to any other ground truth data source of choice. The core of the methodology is an algorithm for maximum pattern matching that works with three fitness objectives. To test the methodology, sample field reference data were collected on multiple freeway segments for a 2-week period by using portable Bluetooth sensors as ground truth. Equivalent GPS probe data were obtained from a private vendor, and their latency was evaluated. Latency at different times of the day, impact of road segmentation scheme on latency, and sensitivity of the latency to both speed-slowdown and recovery-from-slowdown episodes are also discussed.

scholarly journals Examining Travelers’ Acceptance towards Car Sharing Systems—Peshawar City, Pakistan

2019 ◽  
Vol 11 (3) ◽  
pp. 808 ◽  
Author(s):  
Irfan Ullah ◽  
Kai Liu ◽  
Tran Vanduy
Keyword(s):  
Traffic Congestion ◽  
Logit Model ◽  
Urban Areas ◽  
Stated Preference ◽  
Ghg Emissions ◽  
Multinomial Logit Model ◽  
Demographic Characteristics ◽  
Car Ownership ◽  
Nested Logit ◽  
Car Sharing

In recent years, car sharing has emerged as a novel alternative to private car ownership in urban areas worldwide. Potential benefits of this system include improved mobility and reduced congestion, vehicle ownership, parking issues, and greenhouse gas (GHG) emissions. This study aimed to investigate travelers’ acceptance of car sharing systems through a stated preference survey in the city of Peshawar, Pakistan. The questionnaires were distributed online via a Google form. Questions were designed from numerous aspects of car sharing systems, such as awareness of car sharing systems, attributes related to travel modes in the choice set, and demographic characteristics. A total of 453 valid responses were received. The Multinomial and Nested Logit models were employed for evaluation and analysis of survey responses. Demographic characteristics including gender, job, and income were found to be significant. Service attributes including travel time, travel cost, registration fees, and capital cost, were also significant. The multinomial logit model based on both car-owners and non-car-owners fit a little better than the nested logit model. Our findings in the present study could be beneficial for transport planners and policy makers to timely implement car sharing systems in cities in order to mitigate increased car ownership and traffic congestion.

FLOW IMPROVEMENT BY PROVIDING PARTIAL TRAFFIC INFORMATION FOR ROAD USERS

2011 ◽  
Vol 22 (02) ◽  
pp. 181-189 ◽  
Author(s):  
XIAO-YAN SUN ◽  
QING-YI HAO ◽  
MU-REN LIU ◽  
BING-HONG WANG
Keyword(s):  
Traffic Information ◽  
Information Feedback ◽  
Mean Velocity ◽  
Traveler Information ◽  
Traffic Models ◽  
Traveler Information Systems ◽  
Road Users ◽  
Model Average ◽  
Average Flux ◽  
Flow Improvement

In this paper, we investigate two two-route traffic models, i.e. asymmetric and symmetric two-route traffic models, with mean velocity information feedback strategy. The simulation results show that, for asymmetric two-route model, average flux can be significantly improved if only partial road information is provided for road users. However, for symmetric two-route model, average flux of the two routes adopting information feedback cannot outweigh the flux of two routes chosen completely at random. We explain the results by studying the vehicle number on two routes. We hope the study may provide a good suggestion to the design of Advanced Traveler Information Systems.

scholarly journals Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Volker Lücken ◽  
Nils Voss ◽  
Julien Schreier ◽  
Thomas Baag ◽  
Michael Gehring ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Integrated Approach ◽  
Traffic Information ◽  
Limiting Factor ◽  
Personal Mobility ◽  
Real Time Traffic ◽  
Ultrasonic Sensing

Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations.

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