Reducing air pollution from urban passenger transport : a framework for policy analysis

This paper develops a simple framework to analyze various pollution control strategies that have been used or are proposed in the urban passenger transport sector. The context is the declining quality of air in urban areas, which is among the serious problems associated with the rapid motorization of societies the world over. The paper examines the point of impact of different policy levers and provides a categorization of different instruments that should assist policy makers when choosing between them. A distinguishing feature of this framework is its explicit recognition of behavioral incentives, in particular, the fact that offsetting changes in consumer behavior can often undermine the original intent of particular policies. The paper is organized as follows. Section II presents the basic framework we have used to examine transport emissions. Section III reviews pollutant characteristics and their impact. The resulting policy choices are discussed in more detail in section IV. Several urban transport projects supported by the World Bank are then reviewed in section VI, and section V concludes the report.Montreal Protocol,Environmental Economics&Policies,Air Quality&Clean Air,Roads&Highways,Public Health Promotion,Roads&Highways,Urban Transport,Transport and Environment,Environmental Economics&Policies,Airports and Air Services

Promoting walking and cycling as an alternative to using cars: systematic review

Objectives: To assess what interventions are effective in promoting a population shift from using cars towards walking and cycling, and to assess the health and distributional effects of such interventions. Data sources: Published and unpublished reports in any language identified from electronic databases, bibliographies, websites and reference lists. Review methods: Systematic search and appraisal to identify experimental or observational studies with a prospective or controlled retrospective design that evaluated any intervention applied to an urban population or area by measuring outcomes in members of the local population. Results: 22 studies met the inclusion criteria. We found some evidence that targeted behaviour change programmes can change the behaviour of motivated subgroups, resulting (in the largest study) in a modal shift of around 5% of all trips at a population level. Single studies of commuter subsidies and a new railwy station have also shown modest effects. The balance of best available evidence about publicity campaigns, engineering measures and other interventions suggests that they have not been effective. Participants in trials of active commuting experienced short-term improvements in certain health and fitness measures, but we found no good evidence about the health effects of any effective population-level intervention. Conclusions: The best available evidence of effectiveness is for targeted behaviour change programmes, but the social distribution of their effects is unclear and some other types of intervention remain to be rigorously evaluated. We need a stronger evidence base for the health impacts of transport policies, preferably based on properly conducted prospective studies

Non-linear influences of the built environment on transportation emissions: focusing on densities

Compact development is often recommended to reduce auto-dependency thereby decreasing related energy consumptions and transportation emissions. However, there could be a non-linear relationship between density and transportation emissions because of a possible non-linear association between density and vehicle miles travelled (VMT); low travel speed due to congestion; and the relationship between neighborhood characteristics and vehicle characteristics (e.g., vehicle type and age). In addition, the self-selection issue can exist in the land use-transportation emissions analysis because transportation emissions are often estimated based on travel behavior. Using the 2006 Puget Sound Regional Council (PSRC) Household Activity survey, the follow-up stated preference survey, the Motor Vehicle Emission Simulator (MOVES) data, and the GIS network data, this study investigates the non-linear effects of densities on CO2 equivalent (CO2e) emissions with the consideration of self-selection. Specifically, quadratic forms of population and employment densities, different population density group indicators, and attitudinal factors are employed in the regression models. The results indicate that people living in denser neighborhoods tend to generate fewer CO2e emissions. However, this effect becomes insignificant as population density reaches a certain level

Contributing Factors on Drivers Yielding Behaviors at Uncontrolled Intersections

Abstract The present study aims at analyzing drivers yielding behaviors to pedestrians’ right of way, who are attempting to cross at uncontrolled crosswalks. Three types of variables were identified to be collected for this research including characteristics of the locations as well as demographic, and behavioral characteristics of pedestrians and drivers. The behavioral characteristics of drivers and pedestrians is recorded only when a pedestrian arrives at the crosswalks trying to cross and a vehicle is approaching the intersection; so, the driver makes a decision whether or not yield to the pedestrian waiting to cross. Some behavioral characteristics of pedestrians include the pedestrian’s assertiveness, standing location and waiting time at the crosswalk to find a gap in traffic to be able to cross. The demographic characteristics also include age, gender, race. Some location specific variables include the presence of marked crosswalks, pedestrian crossing sign, near side bus stop, right turn lane, whether or not the location has had pedestrian-vehicle crash, type of land use surrounding the un-signalized intersections, crossing distance, AADT, the distance of last car parked from the intersection, the distance difference between the downstream and upstream signalized intersection to the un-signalized intersection, and the last location specific variable is the distance of uncontrolled intersection from the Atwater park locates in eastside of the city nearby the Lake Michigan. After identifying the variables and instructing the data collection process, the location studies were investigated. Twenty un-signalized intersections were selected that specific characteristics were similar among them to maintain consistency across all locations. Ten different uncontrolled intersections are selected as study locations, which each has had at least two pedestrian crashes in 2010 to 2014, and the other ten are selected as comparison locations, which none of them has had any crashes history in the same period of time. To analyze the collected data, five different models are proposed using logistic regression and random effect models. Ultimately, the preferred model that has a better goodness of fit is selected. This model well displays that what variables are most statistically significant with the driver yielding behavior. Based on the final model, each variable may have a positive or negative impact on the driver yielding behavior. The variables that cause drivers yield to the pedestrians at crosswalks include the assertiveness of pedestrians to cross, standing in the street, and the pedestrians’ race with the ethnicity of white as well as the second crosswalk marked, nearside bus stop, and the distance of uncontrolled intersection from the Lake Michigan. Some other independent variables that cause drivers not yield to the pedestrian at crosswalks are the type of land use (commercial area), having a crash history, AADT, crossing distance, and the distance difference between the downstream and upstream signalized intersection to the un-signalized intersection. Note that many professionals cited the importance of land use (proximity to commercial districts, downtown,.etc) on driving yielding behavior because of its relationship with pedestrian volumes. This study does not include a variable representing pedestrian volumes, so that could be explored in future studies. To better illustrate the effect of the variables on the likelihood of the driver yielding, the elasticity analysis was conducted. So, depends on the type of data, they were categorized into continuous and categorical variables. The elasticity from the continuous variable represents that 1% change in crossing distance variable reduces the driver yielding by 15.469%. For categorical variables, the sensitivity of the driver yielding variable is made by pseudo –elasticity. It represents that the existence of the near side bus stop at uncontrolled intersections increases the probability of drivers yielding by 0.54% while the existence of crash history reduces the probability of drivers yielding by 0.82%. It means that drivers still not tend to yield to pedestrians at crashes locations. Eventually, to improve the drivers yielding behaviors at uncontrolled intersections, five E approaches including engineering, enforcement, education, encouragement and evaluation are recommended. The engineering treatments with the minimum cost have a capability of being implemented in a short period of time. Simultaneously, a designed program for applying the law enforcement and for increasing people’s awareness and education in a longer run is anticipated to have a significant impact on improving the drivers yielding behaviors to pedestrians’ right of way at crosswalks. At the end of the program, through evaluation and comparison of the before and after implementation of the engineering, enforcement, education and encouragement strategies, we can determine if the desired result have been met. As part of the focus on enhancing traffic safety and reducing fatal crashes at the assigned locations, High Visibility Enforcement pilot program is also recommended. HVE combines highly visible and proactive law-enforcement strategies to target the violated drivers not yielding to the pedestrian right of way at crosswalks. It offers law enforcement agencies a proven alternative for preventing many of the unsafe driving practices that passenger and drivers engage in on roads. By targeting passenger and drivers, they raise everyone’s awareness of the joint responsibility that we all have to drive carefully and share the road safely

Financial incentives to promote active travel: an evidence review and economic framework

ContextFinancial incentives, including taxes and subsidies, can be used to encourage behavior change. They are common in transport policy for tackling externalities associated with use of motor vehicles, and in public health for influencing alcohol consumption and smoking behaviors. Financial incentives also offer policymakers a compromise between “nudging,” which may be insufficient for changing habitual behavior, and regulations that restrict individual choice.Evidence acquisitionThe literature review identified studies published between January 1997 and January 2012 of financial incentives relating to any mode of travel in which the impact on active travel, physical activity, or obesity levels was reported. It encompassed macroenvironmental schemes, such as gasoline taxes, and microenvironmental schemes, such as employer-subsidized bicycles. Five relevant reviews and 20 primary studies (of which nine were not included in the reviews) were identified.Evidence synthesisThe results show that more-robust evidence is required if policymakers are to maximize the health impact of fiscal policy relating to transport schemes of this kind.ConclusionsDrawing on a literature review and insights from the SLOTH (sleep, leisure, occupation, transportation, and home-based activities) time-budget model, this paper argues that financial incentives may have a larger role in promoting walking and cycling than is acknowledged generally

Driver’s behavior classification in vehicular communication networks for commercial vehicles

Vehicles are becoming more intelligent and connected due to the demand for faster, efficient, and safer transportation. For this transformation, it was necessary to increase the amount of data transferred between electronic modules in the vehicular network since it is vital for an intelligent system’s decision-making process. Hundreds of messages travel all the time in a vehicle, creating opportunities for analysis and development of new functions to assist the driver’s decision. Given this scenario, the dissertation presents the results of research to characterize driving styles of drivers using available information in vehicular communication network. This master thesis focuses on the process of information extraction from a vehicular network, analysis of the extracted features, and driver classification based on the extracted data. The study aims to identify aggressive driving behavior using real-world data collected from five different trucks running for a period of three months. The driver scoring method used in this study dynamically identifies aggressive driving behavior during predefined time windows by calculating jerk derived from the acquired data. In addition, the K-Means clustering technique was explored to group different behaviors into data clusters. Chapter 2 provides a comprehensive overview of the theoretical framework necessary for the successful development of this thesis. Chapter 3 details the process of data extraction from real and uncontrolled environments, including the steps taken to extract and refine the data. Chapter 4 focuses on the study of features extracted from the preprocessed data, and Chapter 5 presents two methods for identifying or grouping the data into clusters. The results obtained from this study have advanced the state-of-the-art of driver behavior classification and have proven to be satisfactory. The thesis addresses the gap in the literature by using data from real and uncontrolled environments, which required preprocessing before analysis. Furthermore, the study represents one of the pioneering studies conducted on commercial vehicles in an uncontrolled environment. In conclusion, this thesis provides insights into the development of driver behavior classification models using real-world data. Future research can build upon the techniques presented in this study and further refine the classification models. The thesis also addresses the threats to validity that were mitigated and provides recommendations for future research

Forecasting the state of health of electric vehicle batteries to evaluate the viability of car sharing practices

Car sharing practices are introducing electric vehicles into their fleet. However, literature suggests that at this point shared electric vehicle systems are failing to reach satisfactory commercial viability. Potential reason for this is the effect of higher vehicle usage which is characteristic for car sharing, and the implication on the battery state of health. In this paper, we forecast state of health for two identical electric vehicles shared by two different car sharing practices. For this purpose, we use real life transaction data from charging stations and different electric vehicles’ sensors. The results indicate that insight into users’ driving and charging behaviour can provide valuable point of reference for car sharing system designers. In particular, the forecasting results show that the moment when electric vehicle battery reaches its theoretical end of life can differ in as much as ¼ of time when vehicles are shared under different conditions

Towards Automotive Embedded Systems with Self-X Properties

With self-adaptation and self-organization new paradigms for the management of distributed systems have been introduced. By enhancing the automotive software system with self-X capabilities, e.g. self-healing, self-configuration and self-optimization, the complexity is handled while increasing the flexibility, scalability and dependability of these systems. In this chapter we present an approach for enhancing automotive systems with self-X properties. At first, we discuss the benefits of providing automotive software systems with self-management capabilities and outline concrete use cases. Afterwards, we will discuss requirements and challenges for realizing adaptive automotive embedded systems

Analysis of time-gap distributions by utilizing the statistical process control concept

This study employed the statistical process control technique to identify high-risk intersections at each time period. The method of this study consisted of three phases. The first phase of the study measured the lateral time gap used by drivers entering a major road stream of traffic from a minor street controlled by a stop sign. The second phase of the study analyzed and interpreted the time-gap data using statistical process control methods. The third phase of the study reported high-risk intersections at each time period and attempted to identify significant factors affecting the selection of time gaps;A total of 27 stop-controlled intersections including two-by-two roads (13 sites) and two-by-four roads (14 sites) throughout Iowa were investigated involving 1,981 drivers. Intersections and their corresponding times were defined as high-risk when they had out-of-control samples each at morning, noon, or evening;The hypothesis test procedure was used as a tool to investigate special variations for the identified high risk intersections at each time period. Several factors including age of drivers, speed limits, types of vehicle, traffic volumes and transmission were related to risk-takings at the stop-controlled intersections. However, gender of drivers and presence of children were not related. Also, accidents reported for observed intersections were compared with high-intersections and the findings revealed that statistical process control techniques was a reliable tool for predicting high-risk intersections at each time period