Effect of traffic composition on capacity of multilane highways

Chandra, Satish; Mehar, Arpan; Velmurugan, S.

doi:10.1007/s12205-015-0479-9

Cited by 26 publications

(23 citation statements)

References 7 publications

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“…It was found that when percentage of heavy vehicle is more, capacity is less and vice versa. Chandra et al [11] found that the capacity of the four-lane divided highway is 4950 vph and it is close to Section I. For the purpose of calibration of capacity, Section-I is taken into consideration for further analysis.…”

Section: Discussionmentioning

confidence: 88%

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Calibration of Vissim Model for Multilane Highways Using Speed Flow Curves

Srikanth¹,

Mehar²,

Parihar³

2017

CEJ

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Traffic flow is a complex phenomenon that needs better understanding of basic concepts and methods for its analysis. As a solution to practical problems, computer simulation has been proved to be a powerful tool in replicating complex traffic systems which allows experimentation to the basic traffic flow system. Various methods are available in literature, some of the methods use statistical tools to verify the difference between simulated and actual outputs and some are based on optimization which tries to minimize the error. For the present work, suitable methodology is adopted for the calibration process. Field data is collected, statistical tests are performed. For the simulation, VISSIM microscopic simulation tool is used and data analysis is performed by considering individual parameters and performance measures like speed, volume and random seed number. Statistical tests have been performed to check the sensitivity of the different simulation parameters. Calibration is done using trial and error method and optimization is performed using solver function. The maximum simulated flow rate was found with default values as 4599 veh/hr, and with calibrated values is 5147 veh/hr which is close to the target capacity 4958 veh/hr as obtained using field composition. Calibrated values of CC0 and CC1 and CC2 parameters were found as most optimized values to achieve target capacity. Finally, validation of calibrated parameter was performed by using different section of a multilane highway.

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Section: Discussionmentioning

confidence: 88%

“…Field capacity is difficult to achieve on highways, because congestion condition is rarely occurring. To calculate field capacity value, literature based method is used which is given by Chandra et al [11] based on the composition of the vehicles. …”

Section: Discussionmentioning

confidence: 99%

Calibration of Vissim Model for Multilane Highways Using Speed Flow Curves

Srikanth¹,

Mehar²,

Parihar³

2017

CEJ

Self Cite

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“…However, a direct empirical method generally requires a survey to be conducted under capacity conditions, and it also is relatively uncommon for a capacity situation to occur on a regular weekday. As a result, micro-simulation models have been widely used for research purposes in indirect empirical analyses in order to estimate highway capacity in previous studies [8,9,[29][30][31][32][33][34], as this reduces the survey limitations and complex phenomena that are difficult to analyze using mathematical techniques [35]. Individual driver behavior simulations can effectively reflect the traffic characteristics in each area and several studies have used traffic micro-simulation models to identify factors affecting roadway capacity.…”

Section: Capacity Estimationmentioning

confidence: 99%

Evaluation of Effect of Median U-Turns on Multilane Primary Highway Capacity in Thailand through Traffic Micro-Simulation Models

Tanwanichkul

Chaipanha

Pitaksringkarn

2018

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This article aims to analyze the effect of median U-turns and estimate the capacity of primary highways in Thailand using a traffic micro-simulation model. Six-lane and four-lane primary highways were selected for the study. The base condition results determined that the maximum capacity of a six-lane primary highway was 2,130 passenger cars/hour/lane, while the four-lane primary highway capacity was recorded as 2,194 passenger cars/hour/lane. Both results were slightly higher than those of the HCM2010 approach. Under prevailing conditions, both sections exhibited lower capacities than the HCM results by approximately 33.7% and 19.8% for the six-lane and fourlane primary highways, respectively, causing the impact of the median U-turn and highway characteristics in Thailand to directly affect traffic and driving behavior. Using the micro-simulation results, an equation was also regressed for estimating the capacity resulting from the impact of the median U-turns and heavy vehicles. These results may be used as guidelines for the design and analysis of multilane highways in Thailand.

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“…Radar speed gun surveys were conducted during nonpeak hours by vehicle type and traffic lane. In the car following model, the time gap (Headway) that has a determining influence on capacity [12,16]. Video data were collected and applied in calibration of the microsimulation model.…”

Section: Data Collection and Preparationmentioning

confidence: 99%

“…A micro-simulation model is often used in indirect empirical analyses to reduce the limitations of the survey and complex phenomena which are difficult to analyze by mathematical techniques. Several previous studies have used traffic micro-simulation models to identify factors affecting roadway capacity [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A traffic micro-simulation model to estimate four-lane highways' capacity in Thailand

2018

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This article purposed to present the maximum capacity and to develop the equation in the capacity estimation of 4 types of four-lane highways by using the micro-simulation model. Regarding the analysis, the factors affecting the capacity include access-point, heavy vehicles and median u-turn. According to the study, it was found that the maximum capacity of the four-lane highways in type 1 is 2194 passenger car/hour/lane. In the descending orders, the maximum capacity of the highways with four lanes in type 2, 3, and 4 are 2161, 2094 and 2017 passenger car/hour/lane, respectively. At the same time, the maximum capacity of the prevailing condition in the study is 1300-1600 vehicles/hour/lane, which is different from the HCM2010 method for 20-30%, due to the Thai's highway characteristics directly affect the traffic and driving behavior. Median u-turn affects the four-lane highways in type 1 and 2 the most, while access-point factor has the most influence towards type 3 and 4. In addition, the author has developed the equation models for capacity estimation, which the result derived from the relationship between the capacities and affecting factors. It was aimed at using as the guidelines in the capacity assessment of Thai's four-lane highways in the future.

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Effect of traffic composition on capacity of multilane highways

Cited by 26 publications

References 7 publications

Calibration of Vissim Model for Multilane Highways Using Speed Flow Curves

Calibration of Vissim Model for Multilane Highways Using Speed Flow Curves

Evaluation of Effect of Median U-Turns on Multilane Primary Highway Capacity in Thailand through Traffic Micro-Simulation Models

A traffic micro-simulation model to estimate four-lane highways' capacity in Thailand

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