Metering Ramps to Divert Traffic around Bottlenecks

2005 ◽  
Vol 1925 (1) ◽  
pp. 12-19 ◽  
Author(s):  
James H. Banks
Keyword(s):  
Travel Time ◽  
Ramp Metering ◽  
Travel Times ◽  
Alternate Route ◽  
Traffic Conditions

Three elementary cases, with ramp metering used to reduce delay by diverting traffic around bottlenecks, are analyzed. In these cases ( a) travel times on an alternate route bypassing the bottleneck are insensitive to flow on the alternate route, ( b) the alternate route is undersaturated but travel times are sensitive to flow, and ( c) the alternate route is oversaturated. Travel time equilibria and traffic assignments are relatively straightforward in all cases provided that equilibria in Cases b and c are assumed to be approximate and traffic assignments are based on drivers’ expectations about traffic conditions prevailing at particular times of day. A metering strategy intended to minimize delay is proposed. This strategy is expressed in terms of the order in which metering is initiated at different ramps and is similar to one previously proposed to maximize output to exits upstream of the bottleneck.

Evaluation of Online Travel Time Estimators and Predictors

2000 ◽  
Vol 1719 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Charles D. R. Lindveld ◽  
Remmelt Thijs ◽  
Piet H. L. Bovy ◽  
Nanne J. Van der Zijpp
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Negative Impact ◽  
Test Site ◽  
Estimation Accuracy ◽  
Travel Times ◽  
Congested Traffic ◽  
Traffic Conditions ◽  
Loop Detectors ◽  
Online Travel

Travel time is an important characteristic of traffic conditions in a road network. Up-to-date travel time information is important in dynamic traffic management. Presented are the findings of a recently completed research and evaluation program called DACCORD, regarding the evaluation of tools for online estimation and prediction of travel times by using induction loop detector data. Many methods exist with which to estimate and predict travel time by using induction loop data. Several of these methods were implemented and evaluated in three test sites in France, Italy, and the Netherlands. Both cross-tool and cross-site evaluations have been carried out. Travel time estimators based on induction loop detectors were evaluated against observed travel times and were seen to be reasonably accurate (10 percent to 15 percent root mean square error proportional) across different sites for uncongested to lightly congested traffic conditions. The evaluation period varied by site from 4 to 30 days. Results were seen to diverge at higher congestion levels: at one test site, congestion levels were seen to have a strong negative impact on estimation accuracy; at another test site, accuracy was maintained even in congested conditions.

scholarly journals Robust Stop-Skipping at the Tactical Planning Stage with Evolutionary Optimization

2019 ◽  
Vol 2673 (3) ◽  
pp. 611-623 ◽  
Author(s):  
Konstantinos Gkiotsalitis
Keyword(s):  
Travel Time ◽  
Solution Space ◽  
Minimax Principle ◽  
Tactical Planning ◽  
Travel Times ◽  
Worst Case ◽  
Planning Stage ◽  
Traffic Conditions ◽  
Time Variations ◽  
Improved Performance

The planning of stop-skipping strategies based on the expected travel times of bus trips has a positive effect in practice only if the traffic conditions during the daily operations do not deviate significantly from those expected. For this reason, we propose a non-deterministic approach which considers the uncertainty of trip travel times and provides stop-skipping strategies which are robust to travel-time variations. In more detail, we show how historical travel-time observations can be integrated into a Genetic Algorithm (GA) that tries to compute a robust stop-skipping strategy for all daily trips of a bus line. The proposed mathematical program of robust stop-skipping at the tactical planning stage is solved using the minimax principle, whereas the GA implementation ensures that improved solutions can be obtained even for high-dimensional problems by avoiding the exhaustive exploration of the solution space. The proposed approach is validated with the use of five months of data from a circular bus line in Singapore demonstrating an improved performance of more than 10% in worst-case scenarios which encourages further investigation of the robust stop-skipping strategy.

Fuzzy Ramp Metering: Design Overview and Simulation Results

1998 ◽  
Vol 1634 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Cynthia Taylor ◽  
Deirdere Meldrum ◽  
Les Jacobson
Keyword(s):  
Fuzzy Logic ◽  
Travel Time ◽  
Fuzzy Controller ◽  
Simulation Software ◽  
Ramp Metering ◽  
Traffic Conditions ◽  
Total Distance ◽  
Total Travel Time ◽  
Vehicle Delay ◽  
Simulation Results

A fuzzy logic ramp-metering algorithm was designed to overcome the limitations of conventional ramp-metering strategies. The fuzzy controller demonstrated improved robustness, prevented heavy congestion, intelligently balanced conflicting needs, and tuned easily. The objective was to maximize total distance traveled and minimize total travel time and vehicle delay, while maintaining acceptable ramp queues. A multiple-ramp study site from the Seattle I-5 corridor was modeled and tested using the freeway simulation software, FRESIM. For five of the six testing sets, encompassing a variety of traffic conditions, the fuzzy controller outperformed the three other controllers tested.

scholarly journals Evaluation of Ramp Metering Impacts on Travel Time Reliability and Traffic Operations through Simulation

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad K. H. Shehada ◽  
Alexandra Kondyli
Keyword(s):  
Travel Time ◽  
Kansas City ◽  
Evaluation Studies ◽  
Ramp Metering ◽  
Travel Time Reliability ◽  
Traffic Operations ◽  
Traffic Conditions ◽  
Wide Range ◽  
Traffic Performance ◽  
Better Than

Ramp metering has been found to improve traffic conditions on the freeway mainline by breaking the platoons of ramp vehicles minimizing turbulence at the merge locations. The majority of the ramp metering evaluation studies have examined traffic performance under specific demand conditions, whereas travel time reliability and variability aspects have not been adequately addressed. This paper focuses on evaluating two well-known ramp metering algorithms in terms of travel time reliability as well as other performance measures such as queue lengths, throughput, and congestion duration, looking at a wide range of traffic demands throughout a calendar year. The evaluation was done through simulating an 8-mile corridor in Kansas City, KS. The results showed localized improvements due to ramp metering at the northern section of the facility, in terms of travel time reliability, throughput, and congestion duration. It was also shown that ramp metering may cause a new (possibly “hidden”) bottleneck to occur downstream, thus diluting its overall benefits when looking at an entire freeway facility. It is further noted that although ALINEA performed better than HERO on the mainline, traffic operations on the on-ramps significantly deteriorated using isolated control.

scholarly journals A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times

2021 ◽  
Author(s):  
Saeed Khanchehzarrin ◽  
Maral Shahmizad ◽  
Iraj Mahdavi ◽  
Nezam Mahdavi-Amiri ◽  
Peiman Ghasemi
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
Time Windows ◽  
Time Dependent ◽  
Mixed Integer ◽  
Travel Times ◽  
Routing Problem ◽  
Traffic Conditions

A new mixed-integer nonlinear programming model is presented for the time-dependent vehicle routing problem with time windows and intelligent travel times. The aim is to minimize fixed and variable costs, with the assumption that the travel time between any two nodes depends on traffic conditions and is considered to be a function of vehicle departure time. Depending on working hours, the route between any two nodes has a unique traffic parameter. We consider each working day to be divided into several equal and large intervals, termed as a scenario. Here, allowing for long distances between some of the nodes, travel time may take more than one scenario, resulting in resetting the scenario at the start of each large interval. This repetition of scenarios has been used in modeling and calculating travel time. A tabu search optimization algorithm is devised for solving large problems. Also, after linearization, a number of random instances are generated and solved by the CPLEX solver of GAMS to assess the effectiveness of our proposed algorithm. Results indicate that the initial travel time is estimated appropriately and updated properly in accordance with to the repeating traffic conditions.

scholarly journals Long-term evaluation of the operational performance of fixed time ramp metering control strategy: a freeway corridor study

2015 ◽  
Vol 42 (11) ◽  
pp. 910-918 ◽  
Author(s):  
Osama Osman ◽  
Julius Codjoe ◽  
Sherif Ishak ◽  
Jose Rodriguez ◽  
Marlene Russell
Keyword(s):  
Travel Time ◽  
Control Strategy ◽  
Traffic Control ◽  
Control Strategies ◽  
Fixed Time ◽  
Ramp Metering ◽  
Level Of Service ◽  
Traffic Conditions ◽  
Entry Points ◽  
Before And After

Ramp metering is one of the successful active traffic control strategies to control traffic flow at entry points to freeways. This study evaluates the effectiveness of fixed-time ramp metering control strategy on the day-to-day operation of traffic over two segments (easternmost and westernmost) of the I-12 corridor in Baton Rouge, Louisiana. Detector speeds and volumes were collected over 11 months and used to generate three performance measures, speed, travel time, and level of service to compare traffic conditions before and after the deployment of ramp meters. Comparative analysis, comprising statistical analysis, analysis of travel time savings, and level of service were then undertaken for traffic conditions before and after ramp meters installation. Overall, the results show some improvements in traffic conditions in the eastbound direction of the westernmost segment; however, the conditions slightly deteriorated on the westbound of the same segment. For the easternmost segment of I-12, no improvement was detected.

Multidimensional Scaling and Application in Traffic Jam Prediction

2013 ◽  
Vol 291-294 ◽  
pp. 3060-3063 ◽  
Author(s):  
Bin Hui Wang ◽  
Lin Cai ◽  
Ming Liu
Keyword(s):  
Multidimensional Scaling ◽  
Travel Time ◽  
Statistical Methods ◽  
Travel Times ◽  
Traffic Jam ◽  
Traffic Conditions ◽  
Informational Technology ◽  
Wide Range ◽  
The Relationship

Many methods thus have been proposed to predict traffic conditions. However, it is difficult to accurately predict traffic jam, because it requires a wide range of knowledge such as statistics and informational technology. It is known that the probability of traffic jam can be evaluated by travel times of passing cars in a location of the motorway. In this paper, we restrict our attention to finding more efficient statistical methods through comparing models. For this reason, we used Multidimensional Scaling statistical methods to study the relationship between traffic conditions and travel time in different locations and times. This work aims at applying basic models to forecast traffic conditions.

scholarly journals “In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. e004318
Author(s):  
Aduragbemi Banke-Thomas ◽  
Kerry L M Wong ◽  
Francis Ifeanyi Ayomoh ◽  
Rokibat Olabisi Giwa-Ayedun ◽  
Lenka Benova
Keyword(s):  
Travel Time ◽  
Friction Surface ◽  
Obstetric Care ◽  
Travel Times ◽  
Night Time ◽  
Google Maps ◽  
Surface Approach ◽  
Low Resource ◽  
Life Saving ◽  
The Cost

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

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