The process of information propagation in a traffic stream with a general vehicle headway: A revisit

Effective inter-vehicle communication is fundamental to a decentralized traffic information system based on mobile ad hoc vehicle networks. Here we model the information propagation process through inter-vehicle communication when the vehicle spacing follows a general i.i.d. distribution. Equations for the expected value and variance of propagation distance are derived. In addition, we provide simple equations for the expected number of vehicles covered and the probability distribution of propagation distance. This research advances on an earlier study where the vehicle spacing is assumed to follow an exponential distribution. This paper generalizes the earlier results and potentially enables a design for robust information propagation by allowing for examination of the impact of different headway distributions. Within the new modeling framework, we also compute connectivity between two vehicles.     

An experimental comparative study of autonomous and co-operative vehicle-follower control systems

This paper is a comparative study of the performance of constant-time-gap autonomous control systems and co-operative longitudinal control systems that use inter-vehicle communication. Analytical results show that the minimum time gap that can be achieved in autonomous control is limited by the bandwidth of the internal dynamics of the vehicle. Experimental results from typical sensors and actuators are used to show that in practice it is very difficult to achieve a time gap less than 1 s with autonomous vehicle following. This translates to an inter-vehicle spacing of 30 m at highway speeds and a theoretical maximum traffic flow of about 3000 vehicles per hour. The quality of radar range and range rate measurements pose limitations on the spacing accuracy and ride quality that can be achieved in autonomous control. Dramatic improvements in the trade-off between ride quality and spacing accuracy can be obtained merely by replacing radar range rate in the autonomous control algorithm with the difference between the measured velocities of the two cars (a rudimentary form of co-operation). As a baseline comparison, the experimental performance of fully co-operative control is presented. An inter-vehicle spacing of 6.5 m is maintained in a platoon of 8 co-operative vehicles with an excellent ride quality and an accuracy of ±20 cm. Extending this to a 10-vehicle platoon makes it possible to achieve theoretical maximum traffic flows of about 6400 vehicles per hour.Another issue of importance addressed in the paper is the need to accommodate malfunctions in radar (ranging sensor) measurements. Measurement errors can occur due to hardware malfunctions as well as due to road curves, grades and the highway environment in the case of large inter-vehicle spacing. The ability of a co-operative control system to monitor the health of the radar and correct for such errors and malfunctions is demonstrated experimentally.     

Experimental and empirical investigations of traffic flow instability

Traffic instability is an important but undesirable feature of traffic flow. This paper reports our experimental and empirical studies on traffic flow instability. We have carried out a large scale experiment to study the car-following behavior in a 51-car-platoon. The experiment has reproduced the phenomena and confirmed the findings in our previous 25-car-platoon experiment, i.e., standard deviation of vehicle speeds increases in a concave way along the platoon. Based on our experimental results, we argue that traffic speed rather than vehicle spacing (or density) might be a better indicator of traffic instability, because vehicles can have different spacing under the same speed. For these drivers, there exists a critical speed between 30 km/h and 40 km/h, above which the standard deviation of car velocity is almost saturated (flat) along the 51-car-platoon, indicating that the traffic flow is likely to be stable. In contrast, below this critical speed, traffic flow is unstable and can lead to the formation of traffic jams. Traffic data from the Nanjing Airport Highway support the experimental observation of existence of a critical speed. Based on these findings, we propose an alternative mechanism of traffic instability: the competition between stochastic factors and the so-called speed adaptation effect, which can better explain the concave growth of speed standard deviation in traffic flow.     

How wide should be the adjacent area to an urban motorway to prevent potential health impacts from traffic emissions?

In recent years, several studies show that people who live, work or attend school near the main roadways have an increased incidence and severity of health problems that may be related with traffic emissions of air pollutants. The concentrations of near-road atmospheric pollutants vary depending on traffic patterns, environmental conditions, topography and the presence of roadside structures. In this study, the vertical and horizontal variation of nitrogen dioxide (NO₂) and benzene (C₆H₆) concentration along a major city ring motorway were analysed. The main goal of this study is to try to establish a distance from this urban motorway considered “safe” concerning the air pollutants human heath limit values and to study the influence of the different forcing factors of the near road air pollutants transport and dispersion. Statistic significant differences (p = 0.001, Kruskal–Wallis test) were observed between sub-domains for NO₂ representing different conditions of traffic emission and pollutants dispersion, but not for C₆H₆ (p = 0.335). Results also suggest significant lower concentrations recorded at 100 m away from roadway than at the roadside for all campaigns (p < 0.016 (NO₂) and p < 0.036 (C₆H₆), Mann–Whitney test). In order to have a “safe” life in homes located near motorways, the outdoor concentrations of NO₂ must not exceed 44–60.0 μg m⁻³ and C₆H₆ must not exceed 1.4–3.3 μg m⁻³. However, at 100 m away from roadway, 81.8% of NO₂ receptors exceed the annual limit value of human health protection (40 μg m⁻³) and at the roadside this value goes up to 95.5%. These findings suggest that the safe distance to an urban motorway roadside should be more at least 100 m. This distance should be further studied before being used as a reference to develop articulated urban mobility and planning policies.     

Simulating the impacts of household travel on greenhouse gas emissions,urban air quality,and population exposure

This paper establishes a link between an activity-based model for the Greater Toronto Area (GTA), dynamic traffic assignment, emission modelling, and air quality simulation. This provides agent-based output that allows vehicle emissions to be tracked back to individuals and households who are producing them. In addition, roadway emissions are dispersed and the resulting ambient air concentrations are linked with individual time-activity patterns in order to assess population exposure to air pollution. This framework is applied to evaluate the effects of a range of policy interventions and 2031 scenarios on the generation of vehicle emissions and greenhouse gases in the GTA. Results show that the predicted increase of approximately 2.6 million people and 1.3 million jobs in the region by 2031 compared to 2001 levels poses a major challenge in achieving meaningful reductions in GHGs and air pollution.     

The cumulative effect of nuisances from road transportation in residential sectors on the Island of Montreal – Identification of the most exposed groups and areas

Air pollution and road traffic noise are considered to be the two most important nuisances that could negatively affect the quality of life. A prolonged exposure to high concentrations of these pollutants could conduct to various health problems. Studies in environmental equity have often considered these nuisances individually whenever it comes from the same source. The main objective of this paper is to determine if the 15 years of age, those aged 65 and over, visible minorities and low income individuals located in a portion of the Island of Montreal are overrepresented in city blocks characterized by having among the highest levels of transportation-related air pollutants (i.e., ambient concentrations of NO₂ and road traffic noise in decibels (dB(A)). The results show that low-income individuals and, to a lesser extent, visible minorities, are significantly overrepresented in city blocks characterized by the higher levels of NO₂ and road traffic noise in dB(A). Multinomial logistic regression analysis confirms these results, and also shows that young people under 15 years are under-represented in the most polluted areas. However, contrary to the previous bivariate results, people aged 65 and over are negatively and significantly associated with the likelihood of their living in a city block located in an advantaged area after controlling for the independent effects of the other explanatory variables. Moreover, visible minorities are significantly overrepresented in advantaged areas. Considering the observed results, some solutions are identified to reduce road traffic noise and air pollution in the city blocks localized near major traffic arteries.     

Roadway determinants of bicyclist exposure to volatile organic compounds and carbon monoxide

Few studies have quantified relationships between bicyclist exposure to air pollution and roadway and traffic variables. As a result, transportation professionals are unable to easily estimate exposure differences among bicycle routes for network planning, design, and analysis. This paper estimates the effects of roadway and travel characteristics on bicyclist exposure concentrations, controlling for meteorology and background conditions. Concentrations of volatile organic compounds (VOC) and carbon monoxide (CO) are modeled using high-resolution data collected on-road. Results indicate that average daily traffic (ADT) provides a parsimonious way to characterize the impact of roadway characteristics on bicyclists’ exposure. VOC and CO exposure increase by approximately 2% per 1000 ADT, robust to different regression model specifications. Exposure on off-street facilities is higher than at a park, but lower than on-street riding – with the exception of a path through an industrial corridor with significantly higher exposure. VOC exposure is 20% higher near intersections. Traffic, roadway, and travel variables have more explanatory power in the VOC models than the CO model. The quantifications in this paper enable calculation of expected exposure differences among travel paths for planning and routing applications. The findings also have policy and design implications to reduce bicyclists’ exposure. Separation between bicyclists and motor vehicle traffic is a necessary but not sufficient condition to reduce exposure concentrations; off-street paths are not always low-exposure facilities.     

Emission evaluation of inter-vehicle safety warning information systems

Driver inattentiveness is one of critical factors contributing to vehicle crashes. The inter-vehicle safety warning information system (ISWS) is a technology to enhance driver attentiveness by providing warning messages about upcoming hazards using connected vehicle environments. A novel feature of the proposed ISWS is its ability to detect hazardous driving events, such as abrupt accelerations and lane changes, which are defined as moving hazards with a higher potential of causing crashes. This study evaluated the effectiveness of the ISWS in reducing vehicle emissions and its potential for traffic congestion mitigation. This study included a field experiment that documented actual vehicle maneuvering patterns for abrupt accelerations and lane changes, which were used for more realistic simulation evaluations, in addition to normal accelerations and lane changes. Probe vehicles equipped with customized on-board units consisting of a global positioning system (GPS) device, accelerometer, and gyro sensor were used to obtain the vehicle maneuvering data. A microscopic simulator, VISSIM, was used to simulate a driver’s responsive behavior when warning messages were delivered. A motor vehicle emission simulator (MOVES) was then used to estimate vehicle emissions. The results show that reduction in vehicle emissions increased when the ISWS’s market penetration rate (MPR) and the congestion level of the traffic conditions increased. The maximum CO and CO₂ emission reductions achieved were approximately 6% and 7%, respectively, under LOS D traffic conditions. The outcomes of this study can be valuable for deriving smarter operational strategies for ISWS to account for environmental impacts.     

Cycleways and footpaths: What separation is needed for equivalent air pollution dose between travel modes?

Cycling and walking are being promoted in many urban areas as alternatives to motorised transport for health, environmental, and financial reasons. The reduced congestion and resulting decrease in the overall amount of pollution reduced can be expected to result in health benefits for the community. However, active commuters, due to their increased respiration rates and often increased travel times can expect to receive larger doses of air pollution compared with those using motorised forms of transport. However, given the large dropoff in concentrations away from a road, it can be expected that significant reductions can be achieved even with relatively small increases in separation between the path of cyclists/pedestrians and motor vehicles.This study presents a simple methodology for calculating the separation needed for cyclists and pedestrians to experience the same air pollution dose as car commuters. An example is given based on carbon monoxide (CO) data collected in a field campaign consisting of a car driver, a cyclist and a pedestrian travelling on a 2600 metre loop of road in Auckland. For this case study, the estimated distance from the centreline needed for cyclists and pedestrians to receive an equivalent dose of CO as motorists was found to range from 5.8 to 14.2 m depending on the commuting mode and the dispersion state of the atmosphere at the site. This was equal to a CO concentration reduction of 0.1–0.14 ppm per metre. Recommendations on facility modifications and route selections have been made to make active mode commuting safer.     

Marginal costs of freeway traffic congestion with on-road pollution exposure externality

The health cost of on-road air pollution exposure is a component of traffic marginal costs that has not previously been assessed. The main objective of this paper is to introduce on-road pollution exposure as an externality of traffic, particularly important during traffic congestion when on-road pollution exposure is highest. Marginal private and external cost equations are developed that include on-road pollution exposure in addition to time, fuel, and pollution emissions components. The marginal external cost of on-road exposure includes terms for the marginal vehicle’s emissions, the increased emissions from all vehicles caused by additional congestion from the marginal vehicle, and the additional exposure duration for all travelers caused by additional congestion from the marginal vehicle. A sensitivity analysis shows that on-road pollution exposure can be a large portion (18%) of marginal social costs of traffic flow near freeway capacity, ranging from 4% to 38% with different exposure parameters. In an optimal pricing scenario, excluding the on-road exposure externality can lead to 6% residual welfare loss because of sub-optimal tolls. While regional pollution generates greater costs in uncongested conditions, on-road exposure comes to dominate health costs on congested freeways because of increased duration and intensity of exposure. The estimated marginal cost and benefit curves indicate a theoretical preference for price controls to address the externality problem. The inclusion of on-road exposure costs reduces the magnitudes of projects required to cover implementation costs for intelligent transportation system (ITS) improvements; the net benefits of road-pricing ITS systems are increased more than the net benefits of ITS traffic flow improvements. When considering distinct vehicle classes, inclusion of on-road exposure costs greatly increases heavy-duty vehicle marginal costs because of their higher emissions rates and greater roadway capacity utilization. Lastly, there are large uncertainties associated with the parameters utilized in the estimation of health outcomes that are a function of travel pollution intensity and duration. More research is needed to develop on-road exposure modeling tools that link repeated short-duration exposure and health outcomes.     

Do cooperative systems make drivers’ car-following behavior safer?

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.     

The role of light duty vehicles in future air pollution: a case study of Sacramento

Abstract

On-road light-duty vehicles (LDVs) play an important role in contributing to urban air pollution. Although vehicles are getting cleaner, regional growth in vehicle population and vehicle miles traveled would somewhat offset California's efforts in transportation pollution reduction. To better understand the role of LDVs in future air pollution, we conduct a case study for Sacramento, California, and investigate future trends in urban air pollution attributable to the light-duty fleet. Results indicate that ambient concentrations of CO, NO _x , and total organic gases (TOGs) caused by future light-duty fleets would dramatically decrease over coming years. The resulting concentrations in 2030 might be as low as approximately 20% of the 2005 concentrations. These reflect the improvements in vehicle/fuel technologies and standards in California. However, the future particulate matter (PM₁₀) pollution could be slightly worse than that caused by the 2005 fleet. This is a result of the growing fleet-average emission factors of particulates from 2005 to 2030. For purposes of future particulate control, more attention needs to be paid to LDVs, besides heavy-duty vehicles.     

Including heavy vehicles in a car‐following model: modelling,calibrating and validating

Heavy vehicles influence general traffic in many different ways compared with passenger vehicles, and this may result in different levels of traffic instability. Increases in the number and proportion of heavy vehicles in the traffic stream will therefore result in different traffic flow conditions. This research initially outlines the different car‐following behaviour of drivers in congested heterogeneous traffic conditions indicating the necessity for developing a car‐following model, which includes these differences. A psychophysical car‐following model, similar in form to Weideman's car‐following model, was developed. Due to the complexity of the developed model, the calibration of the model was undertaken using a particle swarm optimisation algorithm with the data recorded under congested traffic conditions. This was then incorporated into a traffic microsimulation model. The results showed that the car‐following perceptual thresholds and thus action points of drivers differ based on their vehicle and the lead vehicle types. The inclusion of the heavy vehicles in the model showed significant impacts on the traffic dynamic and interactions amongst different vehicles. Copyright © 2016 John Wiley & Sons, Ltd.     

The efficacy of low emission zones in central London as a means of reducing nitrogen dioxide concentrations

This paper considers the effects of different strategies that might be considered to reduce the impact made by road traffic on air pollution in London. The management of road traffic in large urban areas is one of many options being considered to reduce pollutant emissions to meet statutory air pollution objectives. Increasingly, the concept of a low emission zone (LEZ) is being proposed as a means of achieving this reduction. An assessment has been made of different LEZ scenarios in central London, which involve reducing traffic flow or modifying the vehicle technology mix. Methods of predicting annual mean nitrogen dioxide concentrations utilising comprehensive traffic data and air pollution measurements have been used to develop empirical prediction models. Comparisons with statutory air pollution objectives show that significant action will be required to appreciably decrease concentrations of nitrogen dioxide close to roads. The non-linear atmospheric chemistry leading to the formation of nitrogen dioxide, results in a complex relationship between vehicle emissions and ambient concentrations of the pollutant. We show that even ambitious LEZ scenarios in central London produce concentrations of nitrogen oxides that are achieved through a “do nothing” scenario only five years later.     

Driven to injustice? Environmental justice and vehicle pollution in Christchurch,New Zealand

Exposure to an array of air pollutants varies between different social groups. This inequity is one possible explanation for the disparities in health between areas of varying socioeconomic status. However, most studies of vehicle pollution and environmental justice have relied on crude and potentially inaccurate pollution estimates. Using geographically-detailed estimates of traffic-related air pollution, the study investigates whether exposure to pollution in Christchurch, New Zealand varies significantly between areas of different socioeconomic status. The findings suggest that mean exposure to pollution is highest in the most disadvantaged areas of the city. Furthermore, areas where car ownership levels are highest tend to have relatively low levels of pollution exposure. This suggests that there are social injustices in exposure to traffic-related air pollution across neighbourhoods within the urban area of Christchurch.     

Personal exposures to PM during short distance highway travel in India

Traffic is the largest contributor (37%) to urban air pollution in India. During commuting, passengers are significantly exposed to pollutants. We carried out a study on a National Highway (NH) in India to measure personal exposure to Particulate Matter (PM) in five travel modes. PM_2.5 concentrations showed the following trend: Bus > Car FA (fresh air mode of air condition) > Bus AC > Car > Car RC (re-circulation mode of air condition). Highest and lowest concentrations of PM₁₀ were observed in Bus (134 ± 47 µg m⁻³) and Car RC (20 ± 5 µg m⁻³), respectively. The exposures were highest at the rear seats during the Bus AC journeys. In Car FA, the contribution of PM₁ to total concentrations was dominant (61%). Travel modes explained highest variabilities in PM₁₀, PM_2.5 and PM₁ concentrations. In all travel modes, the highest particle counts were observed for PM_0.3–0.5. PM_>0.5–5.0 counts during Bus journeys were comparatively higher than remaining modes. Deposition doses of passengers were as high as 3.22 µg of PM₁₀ (in Bus), 0.66 µg of PM_2.5 (in Bus) and 0.06 µg of PM₁ (in Bus AC) during the ~1 h journey. Our study revealed that Car RC is the safest mode of travel, both in terms of personal exposures and PM depositions in respiratory system. The results from this study can be used to target efforts to reduce personal exposure of highway commuters.     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

Singapore motorisation restraint and its implications on travel behaviour and urban sustainability

The example of Singapore shows that rapid urban and economic growth does not have to bring traffic congestion and pollution. Singapore has chosen to restrain car traffic demand due to its limited land supply. Transport policy based on balanced development of road and transit infrastructure and restraint of traffic has been consistently implemented for the past 30 years. Combined with land use planning, it resulted in a modern transport system, which is free from major congestion and provides users with different travel alternatives. As the economic growth caused a substantial increase in demand for cars, several pricing policies were introduced with the aim of restraining car ownership and usage. Growth of the vehicle population is now controlled and potentially congested roads are subject to road pricing. These measures help to keep the roads free from major congestion, maintain car share of work trips below 25% and keep the transport energy usage low. Although Singapore conditions are in many aspects unique, its travel demand experience can provide useful lessons for other rapidly growing cities in Asia.

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.     

Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system

More than 9 million passengers take Shanghai’s subway system every work day. The system’s air quality has caused widespread concern because of the potential harm to passengers’ health. We measured the particulate matter (PM) concentrations at three kinds of typical underground platform (side-type, island-type, and stacked-type platforms) and inside the trains in Shanghai’s metro during 7 days of measurements in April and July 2015. Our results demonstrated that the patterns of air quality variation and PM concentrations were similar at the side-type and island-type platforms. We also found that the PM concentrations were higher on the platforms than inside the train and that the PM concentrations in the subway system were positively correlated with those in the ambient air. Piston wind generated by vehicle motion pushes air from the tunnel to the platform, so platform PM concentrations increase when trains approach the platform. However, the piston wind effect varies greatly between locations on the platform. In general, the effect of the piston wind is weaker at the middle of the platform than at both ends. PM concentrations inside the train increase after the doors open, during which time dirty platform air floods into the compartments. PM_1.0 and PM_2.5 were significantly correlated both inside the train and on the platforms. PM_1.0 accounted for 71.9% of PM_2.5 inside the train, which is higher than the corresponding platform values. Based on these results, we propose some practical suggestions to minimize air pollution damage to passengers and staff from the subway system.