Assessing the impact of converting roundabouts to traffic signals on vehicle emissions along an urban arterial corridor in Qatar

Deriving environment-friendly speed limit scheme using an integrated traffic simulation framework

Traffic authorities have taken various measures to mitigate vehicular-source pollution emissions, among which setting speed limits has been proved to be effective. However, most of the existing macroscopic research proposes idealized mathematical models to design optimal speed limit from the perspective of network equilibrium, and the simplified pollution emission functions cannot accurately reflect the emission mechanism. In order to improve the accuracy of emission calculation and obtain more reliable speed limit schemes in a large-scale network, an integrated traffic simulation approach was proposed herein to derive environment-friendly road speed limit schemes. First, this article built an integrated simulation framework to portray traffic operations and emissions comprehensively. Subsequently, a toy network was employed to demonstrate how to obtain environment-friendly speed limit schemes using this framework. Then this article utilized it to determine the corresponding optimal speed limit schemes on the Second Ring road network in Beijing under different scenarios. The results indicated that traffic authorities should impose adaptive rather than fixed speed limits according to traffic period and emission reduction priority. Moreover, imposing the derived speed limit schemes, the amounts of total emissions, hydrocarbons, carbon monoxide, nitrogen oxides, carbon dioxide, and fine particulate matter decreased by 2.24%, 0.72%, 0.13%, 1.61%, 2.32%, and 5.35% during peak hour, and by 8.31%, 0.60%, 1.92%, 7.16%, 8.60%, and 8.72% during off-peak hour, respectively.Implications: In order to alleviate traffic pollutions emission issues more effectively from the traffic management perspective, we put forward an integrated traffic simulation approach to derive the environmental friendly road speed limit schemes. We combined MOVES and PARAMICES to build the integrated traffic simulation framework to portray road traffic operations and emissions elaborately. To verify the effectiveness of our method on real large road network, we used this method to find out the corresponding optimal speed limit schemes within the combination scheme set on Second Ring road network in Beijing under different traffic demand scenarios. Comparing with the current speed limit scheme, our derived speed limit scheme contributed to lowering emission. The amounts of total pollution emissions, hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO_x), carbon dioxide (CO₂), and fine particulate matter (PM_2.5) decreased by 2.24%, 0.72%, 0.13%, 1.61%, 2.32%, and 5.35% during peak hours, and by 8.31%, 0.60%, 1.92%, 7.16%, 8.60%, and 8.72% during off-peak hours, respectively.

Evaluation of Microsimulation Models for Roadway Segments with Different Functional Classifications in Northern Iran

Industrialization, urban development, and population growth in the last decades caused a significant increase in congestion of transportation networks across the world. Increasing congestion of transportation networks and limitations of the traditional methods in analyzing and evaluating the congestion mitigation strategies led many transportation professionals to the use of traffic simulation techniques. Nowadays, traffic simulation is heavily used in a variety of applications, including the design of transportation facilities, traffic flow management, and intelligent transportation systems. The literature review, conducted as a part of this study, shows that many different traffic simulation packages with various features have been developed to date. The present study specifically focuses on a comprehensive comparative analysis of the advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN) and SimTraffic microsimulation models, which have been widely used in the literature and practice. The evaluation of microsimulation models is performed for the four roadway sections with different functional classifications, which are located in the northern part of Iran. The SimTraffic and AIMSUN microsimulation models are compared in terms of the major transportation network performance indicators. The results from the conducted analysis indicate that AIMSUN returned smaller errors for the vehicle flow, travel speed, and total travel distance. On the other hand, SimTraffic provided more accurate values of the travel time. Both microsimulation models were able to effectively identify traffic bottlenecks. Findings from this study will be useful for the researchers and practitioners, who heavily rely on microsimulation models in transportation planning.

Critical Gap Comparison between One-, Two-, and Three-Lane Roundabouts in Qatar

A critical gap value of a roundabout is mainly affected by the interactions between the circulating vehicles and vehicles entering the roundabout. These interactions are impacted by many factors, including the number of circulating lanes, the aggressiveness of local drivers, vehicle types, and the number of approaching lanes. Therefore, it is essential to locally investigate critical gap values before conducting any studies to improve capacity and delay at roundabouts. The purpose of this study is to measure and compare the critical gap values for different types of roundabouts in Qatar. More than 10,000 measurements were collected. The results showed that the critical gap values were 2.24 s, 2.55 s, and 2.40 s for the one-, two-, and three-lane roundabouts, respectively. These values are also quite low when compared to values calculated in other countries, which can be an indication of driver aggressiveness and risky behavior. The study is one of the first efforts to compare three types of roundabouts and to understand the difference in operation between them in this region. The results of this study can help engineers, planners, and public agencies to plan, study, and design similar facilities.

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation. Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .