Estimation of highway capacity under environmental constraints vs.conventional traffic flow criteria:A case study of Tehran

In this paper,the concept of environmental capacity is developed to identify a convenient maximum traffic volume which will not reduce the life quality of residents.The presented method investigates the idea of traffic capacity under environmental constraints by calculating the maximum number of vehicles allowed on roads based on acceptable levels of air and noise pollutants.In this study,the permissible noise pollution level and permissible levels of CO and NOxpollution are considered for determining environmental capacity.Results show the significant difference between environmental capacity and functional traffic capacity,introduced by the highway capacity manual(HCM)as a conventional method for estimating functional capacity.Thus,maximum allowed pollution is considered a constraint on a vehicle flow rate,which shows the proper traffic flow for selected streets in Tehran,Iran’s capital.The paper concludes that traffic capacity under noise and air pollution constraints is much less(approximately one-fourth and one-eighth for noise and air pollution respectively)than the current highway capacity estimated using HCM guidelines.Therefore,to save the cities like Tehran from noise and air pollution,traffic flows should be limited to the level of environmental capacity by implementing some travel demand management(TDM)policies like road pricing.

Preface for the special issue on "Driver behavior,highway capacity and transportation resilience"

Transportation plays a vital role both in urban economy and usuallives, which is recognized as one of the major functions of a city, along with dwelling, work, and recreation. As an organic system, urban traffic has attracted increased attention during recent years. However, the majority of studies focused on roadways and vehicular technologies, and limited research has been conducted to address the driver characteristics and their impact. A major possible reason is the scarcity of reliable data. In fact, traditional traffic data obtained from cross-sectional detectors as well as video capture devices are not sufficient to fully capture driver behavior. Only in the recent years, with the availability of transportation-related ＂big data＂ and particu- larly the overwhelming information onboard and from road- side facilities, the impacts of driver behavior may be investigated in more detail. In general, driver behavior in microscopic level may include car-following, lane-changing, and gap acceptance models, which are believed largely to affect roadway capacity. Furthermore, safety concerns especially when drivingin an urban environment due to the interaction of different modes, also increase the complexity of quantifying the quality of service of various transportation facilities. These potential complexities in driver behavior challenge academia to develop supporting models and methods of analysis.

Mixed Traffic Flow Capacity of More Major Lanes Unsignalized Intersection

Highway capacity is defined as maximum volume of traffic flow through the particular highway section under given traffic conditions, road conditions and so on. Highway construction and management is judged by capacity standard. The reasonable scale and time of highway construction, rational network structure and optimal management mode of highway network can be determined by analyzing the fitness between capacity and traffic volume. All over the world, highway capacity is studied to different extent in different country. Based on the gap acceptance theory, the mixed traffic flow composed of two representative vehicle types heavy and light vehicles is analyzed with probability theory. Capacity model of the minor mixed traffic flows crossing m major lanes, on which the traffic flows fix in with M3 distributed headway, on the unsignalized intersection is set up, and it is an extension of minor lane capacity theory for one vehicle-type and one major-lane traffic flow.

Analysis of highway performance under mixed connected and regular vehicle environment

Purpose–This study aims to study the connected vehicle(CV)impact on highway operational performance under a mixed CV and regular vehicle(RV)environment.Design/methodology/approach–The authors implemented a mixed trafficflow model,along with a CV speed control model,in the simulation environment.According to the different traffic characteristics between CVs and RVs,this researchfirst analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases.A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic.To prove this concept,this study simulated the mixed traffic pattern under various conditions.Findings–The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity.Furthermore,a critical CV penetration rate should exist at a specified traffic demand level,which can significantly reduce the speed difference between RVs and CVs.The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance.Originality/value–This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations.CV penetration rate(the proportion of CVs in mixed traffic)is the key factor affecting the impacts of CV on freeway operational performance.The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Performance evaluation of the HCM 2010 platoon dispersion model under midblock pedestrian and truck traffic friction conditions

The urban street segment analysis chapter of the Highway Capacity Manual 2010 provides a methodology for evaluating automobile performance on street segments within an urban roadway network （TRB, 2010）. The methodology involves applying a platoon dispersion model to predict platoon arrival profiles at a downstream signalized intersection; using the predicted arrival flow profiles to compute the proportion of vehicle arrivals on green, and subsequently compute the control delay, average travel speed and level of service （LOS） of the segment. The predictive ability of the HCM 2010 platoon dispersion model has not been independently evaluated using field data. This paper, therefore, uses field data to evaluate the performance of the HCM 2010 platoon dispersion model under friction and no friction traffic conditions. The traffic friction conditions include pedestrian and vehicular interactions on urban collector street segments and the presence of trucks within the traffic stream on principal urban arterial segments. Based on the results obtained, the HCM 2010 platoon dispersion model performs relatively well in predicting platoon arrival profiles on urban street segments with no friction traffic conditions. The results, however, show the model＇s performance is limited on urban street segments with pedestrian and truck friction conditions.