Controlling Congestion in Al-Seeb Highway Using Ramp Metering (Case Study)

This paper represents a case study of traffic congestion within a section on Al Seeb Street highway due to the on-ramp merging of vehicles that causes a bottleneck in the mainline road. It studies the efficiency of installing ramp metering within a ramp within the selected study zone. This is done by simulating the collected data using Vissim software by drawing three one-hour-long scenarios;the first scenario reflects the data collected for 30 minutes duration and is used as a base scenario to draw the other two scenarios, which are reflected as factored-up scenarios to create a situation observed in the early morning in the study zone at 6:00-7:00 in which slowing down of speeds exist, and breakdown is raised in working days. The two factoring-up scenarios were as follows: one without ramp metering and the other without ramp metering. Each scenario was calibrated and run five times with random seeds, and then the average was considered. The simulation examines the ability of RM to smooth traffic in mainline and reduce queuing on on-ramp roads within the selected study zone by comparing the performance of the network for the scenarios and comparing them in terms of the overall delays, number of stops and the average speeds for the vehicles within the mainline. The results showed a good performance reflected by the scenario with ramp metering with a reduction of the overall delay, a decrease in stops number and an increase of the average speed were achieved. For the base scenario, a visualization (video extracted from Vissim software) was extracted, showing no need to install RM with an associated table showing a number of stops equal to zero with an average speed of 102.74 km/h and a total delay of 6045 seconds. For the second scenario with no RM, a visualization was extracted showing a slowing down of speeds for vehicles within the mainline while vehicles merging from the on-ramp and need to be controlled with a table showing a number of stops equal to 16 and an average speed equal to 58 km/h and a total delay of 916,874 seconds. For the third scenario with RM, a visualization was extracted showing good control of the second scenario with a table showing the number of stops equal to 6, an average speed equal to 61 km/h and a total delay equal to 484,466 seconds. Ten literatures in regard to this study have been reviewed. The data collected are quantitative, which are collected using an indirect manual counting method and then the data is used to feed the software for simulation.

Vehicular emissions and concentrations in school zones: A case study

Recent research has revealed that human exposure to air pollutants such as CO, NO_x, and particulates can lead to respiratory diseases, especially among school-age children. Towards understanding such health impacts, this work estimates local-scale vehicular emissions and concentrations near a highway traffic network, where a school zone is located in. In the case study, VISSIM traffic micro-simulation is used to estimate the source of vehicular emissions at each roadway segment. The local-scale emission sources are then used as inputs to the California line source dispersion model(CALINE4) to estimate concentrations across the study area. To justify the local-scale emissions modeling approach, the simulation experiment is conducted under various traffic conditions. Different meteorological conditions are considered for emission dispersion. The work reveals that emission concentrations are usually higher at locations closer to the congested segments, freeway ramps and major arterial intersections. Compared to the macroscopic estimation(i.e. using network-average emission factors), the results show significantly different emission patterns when the local-scale emission modeling approach is used. In particular, it is found that the macroscopic approach over-estimates emission concentrations at freeways and under-estimations are observed at arterials and local streets. The results of the study can be used to compare to the US environmental protection agency(EPA) standards or any other air quality standard to further identify health risk in a fine-grained manner.

Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

Many vehicle platoons are interrupted while traveling on roads,especially at urban signalized intersections.One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure.Thus,this paper develops a Markov chain-based model to recognize platoons.A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability.The videos,recorded with a high-definition camera,contain field driving data from three Tesla vehicles,which can achieve Level 2 autonomous driving.The simulation results show that the recognition rate exceeds 80%when the connected and autonomous vehicle penetration rate is higher than 0.7.Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition.The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.

Queue clearance rate method for estimating passenger car equivalents at signalized intersections

This study explored the use of queue clearance rate method for estimating passenger car equivalent （PCE） at signalized intersections. PCE was estimated based on the assumption that the rate at which a queue of vehicles clears the intersection is a function of its composition. Results of this method were compared with the results estimated by some popular techniques. A fourqegged intersection was simulated in VISSIM software and different techniques were used to convert the traffic mix into a uniform one. Parameters of VISSIM were modified to closely reflect the traffic behaviour under heterogeneous traffic conditions. All approaches of the intersection were loaded to saturated conditions and accuracy of estimated PCEs were established by comparing converted flow （PCE/h） with the capacity of an all-car traffic stream. Method based on saturation flow delivered the best result, but its use was limited to traffic composed only of two types of vehicles. Results of regression and optimization techniques were almost similar and the converted flow was close to the capacity of all-car stream. However, accuracy of these methods strongly relied on the correct measurement of saturation flow. Queue clearance rate method did not require value of saturation flow and yielded good estimates of PCE throughout the simu- lation runs. The maximum difference between the converted flow and capacity estimated with all car situations was found to be less than 10% in all cases considered in this study.