Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows

Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm’s effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway.

Effects of traffic signal coordination on the safety performance of urban arterials

Urban traffic congestion and crashes have been considered by city planners as critical challenges to the economic development of the city. Traffic signal coordination, which connects a series of signals along an arterial by various coordination methodologies, has been proved as one of the most cost-effective means of reducing traffic congestion. In this regard, Metropolitan Planning Organizations (MPO) or Transportation Management Centers (TMC) have included signal timing coordination in their strategic plans. Nevertheless, concerns on the safety effects of traffic signal coordination have been continuously raised by both transportation agencies and the public. This is mainly because signal coordination may increase the travel speed along an arterial, which increases the risk and severity of traffic collisions. To date, there is neither solid evidence from the field to support the concern, nor theoretical-level models to analyze this issue. This research aims to investigate the effects of traffic signal coordination on the safety performance of urban arterials through microsimulation modeling of two traffic operational conditions: free signal operation and coordinated signals, respectively. Three urban arterials in Reno, Nevada were selected as the simulation testbed and were coded in the PTV VISSIM software. The simulated trajectory data were analyzed by the Surrogate Safety Assessment Model (SSAM) to estimate the number of traffic conflicts. Sensitivity analyses were conducted for various traffic demand levels. Results show that under unsaturated conditions, traffic signal coordination could reduce the number of conflicts in comparison with the free signal operation condition. However, under oversaturated conditions, no significant difference was found between coordinated and free signal operations. Findings from this research indicate that traffic signal coordination has the potential to reduce the risk of crashes on urban arterials under unsaturated conditions.

Optimization of Emergency Transportation Organization of Holiday Tourism Traffic

The purpose of the optimization of holiday traffic emergency traffic organization is to solve the problem of serious traffic jams in holiday scenic spots. Based on the prediction of traffic volume and traffic mode division in the future years of the scenic spot, the traffic accident route is analyzed to provide theoretical support for the emergency traffic organization and planning of the scenic spot. This article takes the Shijiazhuang Jinta Bay scenic area as the research object, based on the traffic volume of the Jinta Bay tourist scenic area from 2009 to 2016, analyzes the traffic environment of the scenic area, predicts the traffic demand, and builds a one-way traffic organization double-layer optimization model. The simulated annealing algorithm is used to solve the model, an emergency transportation organization optimization plan is formulated, and the feasibility of the plan is verified through VISSIM simulation. The results of the study show that the one-way traffic organization method reduces the average vehicle delay by 32.2% and the average queue length by 14.5%. The one-way traffic organization based on branch diversion can more effectively solve the main road jamming and congestion caused by traffic accidents, prevent the occurrence of secondary accidents, and reduce the economic losses of scenic area managers. At the same time, the purpose of ensuring the tourist quality of tourists and the economic interests of scenic spot management departments is ensured.

Network Design Algorithm Implementation for Resilient Transportation System under Continuous Risk Perturbation with Big Data Analysis

With the rapid economic development and urbanization process accelerating, motor vehicle ownership in large cities is increasing year by year; urban traffic congestion, parking difficulties, and other problems are becoming increasingly serious; in ordinary daily life, continuous risk of disturbance, having a flexible transportation system network is more able to alleviate daily congestion in the city, and the main thing about flexible transportation network is its algorithm. It is worth noting that congestion in many cities is generally reflected in the main roads, while many secondary roads and branch roads are underutilized, and the limited road resources in cities are not fully utilized. As an economic and effective road traffic management measure, one-way traffic can balance the spatial and temporal distribution of traffic pressure within the road network, make full use of the existing urban road network capacity, and solve the traffic congestion problem. Therefore, it is of great theoretical and practical significance to develop a reasonable and scientific one-way traffic scheme according to the characteristics of traffic operation in different regions. Based on the fixed demand model, the influence of traffic demand changes is further considered, the lower-level model is designed as an elastic demand traffic distribution model, the excess demand method is used to transform the elastic demand problem into an equivalent fixed demand problem based on the extended network, and the artificial bee colony algorithm based on risk perturbation is designed to solve the two-level planning model. The case study gives a one-way traffic organization optimization scheme that integrates three factors, namely, the average load degree overload limit of arterial roads, the detour coefficient, and the number of on-street parking spaces on feeder roads, and performs sensitivity analysis on the demand scaling factor.

Signal Control Strategy for Supersaturated Traffic at Urban Road Intersections with a Large Technical Grade Gap

To reduce the risk of queuing overflow on the urban minor road at the intersection under supersaturation where the capacity of the arterial and minor roads shows extreme disparity, reduce the adverse effects caused by long queues of vehicles on the minor road, and comprehensively balance the multiobjective requirements such as priority of the main road, queuing restrictions, and delay on the minor road, the minor road queue model at the end of red, a road remaining capacity model, and multiparameter coordinated signal control model were established, and a multiobjective genetic algorithm was used to optimize this solution. As an example, the multiparameter coordinated control strategy decreased the delay per vehicle by approximately 17% and the queue length by approximately 30%–50% on the minor road and slightly increased the delay per vehicle at the intersection and the length on the main road queue. This control strategy can make full use of the capacity of the main road to control the queue length on the minor road, effectively reduce the risk of minor road queue overflow blocking local road network traffic operation involved, and comprehensively balance the traffic demand between arterial and minor roads. It provides a reference control method for coping with the transfer of the main traffic contradiction under the oversaturated state of the road intersection with large disparity.

Long-term relationship estimation and coupling/decoupling analysis between motorway traffic and Gross Value Added. Specification of an ARDL cointegration approach and application to the Italian case study

As transportation is an activity derived from spatial complementarities between a certain supply at an origin and a certain demand at a destination, according to a general axiom it seems that economic activities entail transport de-mand. In this perspective, an essential analysis deals with the quantification of the relationships between transport demand and certain socioeconomic variables. Elasticity is a concept widely used in transport economics as a measure of the responsiveness of transport demand concerning different factors represented as independent variables in an econometric model and coupling/decoupling concepts have been proposed in literature. This paper deals with the estimation of elasticities of motorway traffic demand based on Gross Value Added (GVA), and the consequent investiga-tion of coupling/decoupling situation. The analysis is based on the application of an Autoregressive-Distributed Lag (ARDL) cointegration model with the F-bound test and of the related Error Correction model. Starting from the general ARDL model and the methodology for the verification of its robustness, the same model is applied to the Italian toll road network. The time series of GVA for goods and services and the overall length of the toll network from 1995 to 2019 are considered as explanatory variables of the total annual distance traveled by light and heavy vehicles. The various tests in the ARDL framework show a cointegration between the variables, under the fulfillment of all the diag-nostic requirements. In this way, the long-term elasticities and the short-term adjustment dynamics are estimated sepa-rately for the goods and services components of GVA, and light and heavy vehicles. Starting from stable estimates of elasticities, the long-term coupling and decoupling effects between motorway traffic of light and heavy vehicles and the national production of goods and services can be shown. The paper, as well as providing an updated picture of the Italian situation, identifies a methodological framework that can be transferred to other contexts for a sector of great interest to investors, such as the motorway sector. All this can be useful to meet the needs of numerous stakeholders, who want to deepen the links between the economic cycle and traffic demand on toll motorways.

Determination of Land Fund for the Development of Static Road Traffic Demand in Hanoi (Vietnam)

Determining a reasonable proportion of land fund for urban static traffic will meet the currentneeds and future development of urban areas, contributing to improving the operational quality of urbantransport systems and improve the quality of life of people in urban areas [1]. Hanoi, the capital of VietNam, is facing difficulties in meeting the land fund for static traffic development. In 2020, the city's landfund meets only 38.73% of the demand for traffic system development in general, static traffic in particular[2]. By using a regression model on the relationship between GDP per capita and demand for means oftransport in Hanoi, the article forcats the demand for urban static traffic development in Hanoi city,demand demand for land fund for its static traffic development to 2025 and 2030. From the forecast results,the article proposes some solutions on meeting the land fund demand for static traffic development inorder to achieve efficiency of the government's policis on static traffic development in Hanoi.

On Site Improvement of Fines-Rich Unbound Granular Materials with Hydrophobic Polymer and Lime

Many roads that were initially designed for relatively low traffic volumes need re-surfacing or partial replacement of the unbound granular material to satisfy current traffic demand. Significant research efforts based on laboratory studies have been seen in the literature to characterize the suitability of virgin materials, which is relatively expensive and unsustainable. Therefore, the object of this study is the in situ recycling of existing materials in two road sections by improving their properties with a suitable additive. A hydrophobic synthetic polymer was chosen for two trials due to the high plasticity of fines of the in situ materials and a high chance of water intrusion in the low-lying plains in Adelaide. The extensive laboratory characterization shows that hydrophobicity is imparted in capillary rise tests, improved drainage in permeability tests, and greater matric suction at the same moisture content. Furthermore, the unconfined compressive strength was increased. The repeated loading triaxial testing showed higher stiffness and lowered permanent strain to withstand higher traffic volume. In general, in situ recycling is adaptable and considered to be cheaper and sustainable. The estimated current costs and carbon footprints are presented for re-construction and in situ recycling with dry powder polymer, or solely with lime, to help construction planning.

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.