Evaluation of Compliance Rates and Travel Time Calculation for Automatic Alternative Route Guidance Systems on Freeways

1996 ◽  
Vol 1554 (1) ◽  
pp. 153-161 ◽  
Author(s):  
Reinhart D. Kühne ◽  
Karin Langbein-Euchner ◽  
Martin Hilliges ◽  
Norbert Koch
Keyword(s):  
Travel Time ◽  
Compliance Rate ◽  
Measurement Data ◽  
High Sensitivity ◽  
Guidance System ◽  
Route Guidance ◽  
Alternative Route ◽  
Travel Times ◽  
Main Road ◽  
Guidance Systems

This study outlines the concept of extending an available simulation model for evaluation of freeway route guidance systems using the compliance rates of drivers with alternative route recommendations based on measurements from the freeway subnetwork near Munich, Germany. The system works with variable direction signs that automatically display routing instructions to prevent congestion on the main road. The effectiveness of the system is assessed by calculating the travel times with and without an alternative route guidance system in operation. The result is a decrease in individual travel times on the main road and overall travel time savings for all traffic participants of the system. The simulation indicates a high sensitivity of diverting portions of traffic that allows an exact validation. The diverted traffic affects not only travel time and the congested area but also the destinations, which permits the use of the compliance rate as an accurate fit parameter for exact description of traffic patterns from measurement data.

MACHINE LEARNING ON CONGESTION ANALYSIS BASED REAL-TIME NAVIGATION SYSTEM

2011 ◽  
Vol 20 (04) ◽  
pp. 753-781
Author(s):  
KAI CHEN ◽  
KIA MAKKI ◽  
NIKI PISSINOU
Keyword(s):  
Machine Learning ◽  
Travel Time ◽  
Real Time ◽  
Navigation System ◽  
Prediction Models ◽  
Urban Traffic ◽  
Route Guidance ◽  
Highway Traffic ◽  
Data Set ◽  
Guidance Systems

In the metropolitan region, most congestion or traffic jams are caused by the uneven distribution of traffic flow that creates bottleneck points where the traffic volume exceeds the road capacity. Additionally, unexpected incidents are the next most probable cause of these bottleneck regions. Moreover, most drivers are driving based on their empirical experience without awareness of real-time traffic situations. This unintelligent traffic behavior can make the congestion problem worse. Prediction based route guidance systems show great improvements in solving the inefficient diversion strategy problem by estimating future travel time when calculating accurate travel time is difficult. However, performances of machine learning based prediction models that are based on the historical data set degrade sharply during a congestion situation. This paper develops a new navigation system for reducing travel time of an individual driver and distributing the flow of urban traffic efficiently in order to reduce the occurrence of congestion. Compared with previous route guidance systems, the results reveal that our system, applying the advanced multi-lane prediction based real-time fastest path (AMPRFP) algorithm, can significantly reduce the travel time especially when drivers travel in a complex route environment and face frequent congestion problems. Unlike the previous system,1 it can be applied either for single lane or multi-lane urban traffic networks where the reason for congestion is significantly complex. We also demonstrate the advantages of this system and verify the results using real highway traffic data and a synthetic experiment.

Real-time travel time predictor for route guidance consistent with driver behavior

2012 ◽  
Vol 39 (10) ◽  
pp. 1113-1124 ◽  
Author(s):  
Tian-dong Xu ◽  
Yuan Hao ◽  
Zhong-ren Peng ◽  
Li-jun Sun
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Estimation ◽  
Field Testing ◽  
Pilot Project ◽  
Time Travel ◽  
Guidance System ◽  
Route Guidance ◽  
Travel Time Prediction ◽  
Traffic Conditions

Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.

When Should Auditory Guidance Systems Tell Drivers to Turn?

1995 ◽  
Vol 39 (17) ◽  
pp. 1072-1076 ◽  
Author(s):  
Paul Green ◽  
Kellie George
Keyword(s):  
Regression Analysis ◽  
Navigation System ◽  
Traffic Signal ◽  
Guidance System ◽  
Vehicle Navigation ◽  
Route Guidance ◽  
Vehicle Navigation System ◽  
Guidance Systems ◽  
Turn Direction

This two-part experiment examined how far from an intersection an auditory route-guidance system should present the final turn instructions (e.g., “Turn right.”). In part 1, 48 drivers followed instructions from a simulated in-vehicle navigation system (“In approximately 2 miles, turn right at the traffic signal”), responding “Is this it?” when they thought they had reached the desired intersection. In response, the computer gave the appropriate guidance (“No, continue…” or “Turn…”). In part 2, they repeatedly approached 2 different intersections. Feedback from previous trials (“too far,” “too close,” “OK”) was used to adjust when messages (e.g., “Turn left.”) were presented. Regression analysis revealed that last turn messages should be provided approximately 450 feet before an intersection (approached at 40 mi/h), with that value being adjusted 15 feet for each mile per hour change. Adjustments are also made for gender (plus or minus 56 feet), age (plus or minus 60 feet), and turn direction (plus or minus 48 feet).

Driver En Route Guidance Compliance and Driver Learning with Advanced Traveler Information Systems: Analysis with Travel Simulation Experiment

2003 ◽  
Vol 1843 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Wan-Hui Chen ◽  
Paul P. Jovanis
Keyword(s):  
Linear Mixed Model ◽  
Simulation Experiment ◽  
Compliance Rate ◽  
Route Choice ◽  
Guidance System ◽  
Route Guidance ◽  
Temporal Experience ◽  
Spatial Experience ◽  
Factors Affecting ◽  
Significant Factors

If real-time driver en route guidance advice does not meet driver preferences (e.g., preference for taking the freeway) or the advice is not correct, drivers are very likely to ignore the information, and the guidance system becomes ineffective in their route choice no matter how advanced the system. There is a need to investigate the factors affecting driver compliance with en route guidance advice. A travel simulation experiment was used to investigate significant factors affecting driver route choice behavior. A linear mixed model was developed for describing the factors affecting driver compliance with guidance advice using the compliance rate over several simulated trips as a dependent variable. The issue of repeated observations is addressed. The system accuracy and subjects’ learning experience in their spatial experience at the same intersection and temporal experience in the same day are also taken into account. The model results show that significant factors are involved: freeway advice, turning advice, congestion occurrence, incident occurrence, subjects’ spatial experience, subjects’ temporal experience, and subjects’ education level; there are several important interactions as well.

Real-Time Multiple-Objective Path Search for In-Vehicle Route Guidance Systems

1997 ◽  
Vol 1588 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Victor J. Blue ◽  
Jeffrey L. Adler ◽  
George F. List
Keyword(s):  
Travel Time ◽  
Simulation Modeling ◽  
Search Algorithm ◽  
Multiple Objective ◽  
Route Guidance ◽  
Market Penetration ◽  
Vehicle Route ◽  
Trade Offs ◽  
Path Search ◽  
Guidance Systems

The application of multiple-objective route choice for in-vehicle route guidance systems is discussed. A bi-objective path search algorithm is presented and its use demonstrated. A concept of trip quality is introduced that is composed of two objectives: minimizing travel time and minimizing trip complexity. Trade-offs between the objectives are examined. The concept is illustrated through simulation modeling on a test network. The experiments serve to demonstrate the effects on the trip performance of pretrip routing and dynamic routing strategies under full market penetration (an idealized condition) and under varying levels of demand and trade-offs between time and complexity.

Evaluating Effectiveness of Real-Time Advanced Traveler Information Systems Using a Small Test Vehicle Fleet

1997 ◽  
Vol 1588 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Christopher L. Saricks ◽  
Joseph L. Schofer ◽  
Siim Sööt ◽  
Paul A. Belella
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Of Day ◽  
Guidance System ◽  
Effective Characteristics ◽  
Local Network ◽  
Route Guidance ◽  
Time Data ◽  
Traveler Information ◽  
Link Travel Time

ADVANCE was an in-vehicle advanced traveler information system (ATIS) providing route guidance in real time that operated in the northwestern portion and northwest suburbs of Chicago, Illinois. It used probe vehicles to generate dynamically travel time information about expressways, arterials, and local streets. Tests to evaluate the subsystems of ADVANCE, executed with limited availability of test vehicles and stringent scheduling, are described; they provided useful insights into both the performance of the ADVANCE system as a whole and the desirable and effective characteristics of ATIS deployments generally. Tests found that the user features of an in-route guidance system must be able to accommodate a broad range of technological sophistication and network knowledge among the population likely to become regular users of such a system. For users who know the local network configuration, only a system giving reliable real-time data about nonrecurrent congestion is likely to find a market base beyond specialized applications. In general, the quality and usefulness of systemwide real-time route guidance provided by other means are enhanced significantly by even a small deployment of probes: probe data greatly improve static (archival average) link travel time estimates by time of day, although the guidance algorithms that use these data should also include arterial traffic signal timings. Moreover, probe- and detector-based incident detection on arterial networks shows considerable promise for improved performance and reliability.

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