Author: Publisher:
ISBN:
Category : Automobile drivers
Languages : en
Pages : 226
Book Description
The Impact of Real-time and Predictive Traffic Information on Travelers' Behavior in the I-4 Corridor
Author: Public Roads
Author: Traffic Simulation Along the I-4 Central Corridor
Author: Prediction of Traffic Conditions Along the I-4 Central Corridor
Author: Annual Report
Author: Expanding Sphere of Travel Behaviour Research
Author: Ryuichi KitamuraPublisher: Emerald Group Publishing
ISBN: 1848559372
Category : Psychology
Languages : en
Pages : 955
Book Description
Suitable for researchers, and graduate students in the field of transportation and urban planning in general, and in travel behaviour analysis in particular, this volume of the 11th International Conference on Travel Behaviour Research, held in Kyoto, Japan, in August 2006, examines key issues and emerging trends in the field of travel behaviour.
Forecasting Travel in Urban America
Author: Konstantinos ChatzisPublisher: MIT Press
ISBN: 0262048108
Category : Technology & Engineering
Languages : en
Pages : 417
Book Description
A history of urban travel demand modeling (UTDM) and its enormous influence on American life from the 1920s to the present. For better and worse, the automobile has been an integral part of the American way of life for decades. Its ascendance would have been far less spectacular, however, had engineers and planners not devised urban travel demand modeling (UTDM). This book tells the story of this irreplaceable engineering tool that has helped cities accommodate continuous rise in traffic from the 1950s on. Beginning with UTDM’s origins as a method to help plan new infrastructure, Konstantinos Chatzis follows its trajectory through new generations of models that helped make optimal use of existing capacity and examines related policy instruments, including the recent use of intelligent transportation systems. Chatzis investigates these models as evolving entities involving humans and nonhumans that were shaped through a specific production process. In surveying the various generations of UTDM, he delves into various means of production (from tabulating machines to software packages) and travel survey methods (from personal interviews to GPS tracking devices and smartphones) used to obtain critical information. He also looks at the individuals who have collectively built a distinct UTDM social world by displaying specialized knowledge, developing specific skills, and performing various tasks and functions, and by communicating, interacting, and even competing with one another. Original and refreshingly accessible, Forecasting Travel in Urban America offers the first detailed history behind the thinkers and processes that impact the lives of millions of city dwellers every day.
Human Factors Opportunities to Improve Ohio's Transportation System
Author: Effect of Freeway Corridor Attributes Upon Motorist Diversion Responses to Real-time Travel Time Traffic Information
Author: Gerald L. UllmanPublisher:
ISBN:
Category : Highway communications
Languages : en
Pages : 74
Book Description
This report describes the resultes of laboratory experiments performed to assess whether certain freeway corridor attributes specified in a real-time motorist information display affect motorists' expected reponses to travel time information. Specifically, the study was designed to determin whether (1) the recommended alternative route. (2) the location where motorists were told to divert from the freeway, or (3) the location of the reported onset of congestion relative to where motorists were told to divert influenced motorist diversion threshold values to time saved travel time information. The procedures and results of the studies are described, and recommendations are presented for application of the resultes to the design and operation of freeway corridor motorist information displays.
Real Time Prediction of Traffic Speed and Travel Time Characteristics on Freeways
Author: Reza NoroozisananiPublisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 129
Book Description
Travel time is one of the important transportation performance measures, and represents the quality of service for most of the facilities. In other words, one of the essential goals of any traffic treatment is to reduce the average travel time. Therefore, extensive work has been done to measure, estimate, and predict travel time. Using historical observations, traditional traffic analysis methods try to calibrate empirical models to estimate the average travel time of different transportation facilities. However, real-time traffic responsive management strategies require that estimates of travel time also be available in real-time. As a result, real time estimation and prediction of travel time has attracted increasing attention. Various factors influence the travel time of a road segment including: road geometry, traffic demand, traffic control devices, weather conditions, driving behaviors, and incidents. Consequently, the travel time of a road segment varies as a result of the variation of the influencing factors. Predicting near-future freeway traffic conditions is challenging, especially when traffic conditions are in transition from one state to another (e.g. changing from free flow conditions to congestion and vice versa). This research aims to develop a method to perform real-time prediction of near-future freeway traffic stream characteristics (namely speed) and that relies on spot speed, volume, and occupancy measurements commonly available from loop detectors or other similar traffic sensors. The framework of this study consists of a set of individual modules. The first module is called the Base Predictor. This module provides prediction for traffic variables while state of the traffic remains constant i.e free flow or congested. The Congestion Detection Module monitors the traffic state at each detector station of the study route to identify whether traffic conditions are congested or uncongested. When a congestion condition is detected, the Traffic Propagation Module is activated to update the prediction results of the Steady-State Module. The Traffic Propagation Module consists of two separate components: Congestion Spillback activates when traffic enters a congested state; Congestion Dissipation is activated when a congested state enters a recovery phase. The proposed framework of this study is calibrated and evaluated using data from an urban expressway in the City of Toronto, Canada. Data were obtained from the westbound direction of the Gardiner Expressway which has a fixed posted speed limit of 90 km/hr. This expressway is equipped with mainline dual loop detector stations. Traffic volume, occupancy and speed are collected every 20 seconds for each lane at all the stations. The data set used in this study was collected over the period from January 2009 to December 2011. For the Steady-State Module, a model based on Kalman filter was developed to predict the near future traffic conditions (speed, flow, and occupancy) at the location of fixed point detectors (i.e. loop detector in this study). Traffic propagation was proposed to be predicted based on either a static or dynamic traffic pattern. In the static pattern it was assumed that traffic conditions can be attributed based on the observed conditions in the same time of day; however, in the dynamic pattern, expected traffic conditions are estimated based on the current measurements of upstream and downstream detectors. The prediction results were compared to a naïve method, and it was shown that the average prediction error during the “change period” when traffic conditions are changing from free flow to congestion and vice versa is significantly lower when compared to the naïve method for the sample locations (approximately 25% improvement) For the Traffic Propagation Module, a model has been developed to predict the speed of backward forming and forward recovery shockwaves. Unlike classic shockwave theory which is deterministic, the proposed model expresses the spillback and recovery as a stochastic process. The transition probability matrix is defined as a function of traffic occupancy on upstream and downstream stations in a Markov framework. Then, the probability of spillback and recovery was computed given the traffic conditions. An evaluation using field data has shown that the proposed stochastic model performs better than a classical shockwave model in term of correctly predicting the occurrence of backward forming and forward recovery shockwaves on the field data from the urban expressway. A procedure has been proposed to improve the prediction error of a time series model (Steady-State Module) by using the results of the proposed Markov model. It has been shown that the combined procedure significantly reduces the prediction error of the time series model. For the real-time application of the proposed shockwave model, a module (Congestion Detection Module) is required to simultaneously work with the shockwave model, and identify the state of the traffic based on the available measurements. A model based on Support Vector Machine (SVM) was developed to estimate the current traffic state based on the available information from a fixed point detector. A binary model for the traffic state was considered i.e. free follow versus congested conditions. The model was shown to perform better compared to a Naïve model. The classification model was utilized to inform the Traffic Propagation Module. The combined model showed significant improvement in prediction error of traffic speed during the “Change Period” when traffic conditions are changing from free flow to congestion and vice versa. Variability of travel speed in the near future was also investigated in this research. A continuous-time Markov model has been developed to predict the state of the traffic for the near future. Four traffic states were considered to characterize the state of traffic: two free flow states, one transition state, and one congested state. Using the proposed model, we are able to predict the probability of the traffic being in each of the possible states in the near future based on the current traffic conditions. The predicted probabilities then were utilized to characterize the expected distribution of traffic speed. Based on historical observations, the distribution of traffic speed was characterized for each traffic state separately. Given these empirical distributions and the predicted probabilities, distribution of traffic speed was predicted for the near future. The distribution of traffic speed then was used to predict a confidence interval for the near future. The confidence interval can be used to identify the expected range of future speeds at a given confidence level.