A study of motorcyclist's idling stop behavior at red lights

Abstract

Motorcycle activity in Asian economies is a significant contributor to carbon dioxide (CO₂) emissions, both when moving and when idling at traffic lights. This paper investigates Taiwanese motorcyclists’ behavior of turning off the idling engine while stopping at traffic lights based on the theory of planned behavior (TPB), which recognizes that the achievement of voluntary change behavior can be identified by knowing an individual's attitudes (or behavioral intentions [BIs]) in the context of social norms (SN). A structural equation model system is used to identify candidate causal links between attitudes, SN, BI and behavior related to the idling stop behavior of motorcyclists. A partial least squares (PLS) model is built to correct the covariance matrix, given the relatively small sample size. Results suggest that attitudes, SN and perceived behavioral control, are significant determinants of idling stop BI at red lights.     

The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data

Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people’s interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users’ interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines.     

Rethinking the links between social exclusion and transport disadvantage through the lens of social capital

This paper provides a critical review of the progress in understanding the linkages between transport disadvantage and social exclusion. It follows earlier work in proposing social capital as a concept that mediates those linkages but argues that transport researchers must not confine themselves to conceptualisations of social capital as predominantly benign and capable of reducing transport disadvantage and social exclusion. A range of hypothetical pathways is discussed, highlighting the Janus-faced character of social capital as a medium for both the effectuation of progressive social change and the perpetuation and creation of social inequalities. An analysis is provided of the extent to which the recent transport-related literature supports or rejects the hypothesised pathways, and key avenues for future research are identified.     

Role model advocacy for sustainable transport

Individual aspirations of associating with role models are routinely harnessed by marketers, who for instance, use celebrity endorsement in selling brands and products. It appears there has been no research to date, however, on the potential for celebrity activism, or role model advocacy beyond celebrities, such as from politicians, to form effective interventions for encouraging sustainable transport behavior. This is despite studies suggesting that celebrity endorsement is a potential gateway for transforming public opinion on carbon intensive transport modes. The present paper consequently offers a critical review of the literature on role model advocacy and celebrity activism, and how these concepts have been harnessed to address environmental issues, in order to conceptually assess the potential for extending these intervention techniques to the context of sustainable transport. The scope of the paper includes the potential that high profile politicians/celebrities might play as role models in exercising referent power to influence social norms surrounding sustainable transport, given that the success of social marketing interventions are closely tied to the need for changes in the policy landscape. Key dimensions of role model endorsement in transport are identified and applied to a series of examples of how celebrity and political role models have influenced transport cultures. In addition to offering an original application of a theoretical framework to a new context, in order to help address the increasingly important societal issue of transport’s growing contribution to climate change, the paper discusses the challenges associated with the neoliberal framing of this approach.     

Application of multi-criteria decision analysis methods for assessing walkability: A case study in Porto Alegre,Brazil

The concept of walkability refers to the extent to which a neighbourhood is walking-friendly. Several walkability indexes have been developed to quantify and evaluate the pedestrian environment. These indexes differ in terms of type of data, methods and goals. The indexes variables may present either uniform or distinct weights, defined by arbitrary, empirical or other diverse weighting methods. This paper pursues the determination of a weighted walkability index, constructed on the basis of the relative importance of their attributes. Weights were determined by the application of the Fuzzy Analytic Hierarchy Process (FAHP), a robust multi-criteria method which considers the experts’ uncertainty in decision making. Moreover, FAHP weights were compared with the attribute weights obtained from other simpler methods, and a chi-square test for homogeneity was computed to compare the obtained values. The three most important walkability attributes were: Public Security, Traffic Safety and Pavement Quality, similar results to the ones found in the literature. The application to a case study in the city of Porto Alegre, Brazil, allowed categorizing the studied neighbourhoods and to analyse the effect of changes on attributes in walkability.     

An equilibrium analysis of commuter parking in the era of autonomous vehicles

This study is the first in the literature to model the joint equilibrium of departure time and parking location choices when commuters travel with autonomous vehicles (AVs). With AVs, walking from parking spaces to the work location is not needed. Instead, AVs will drop off the commuters at the workplace and then drive themselves to the parking spaces. In this context, the equilibrium departure/arrival profile is different from the literature with non-autonomous vehicles (non-AVs). Besides modeling the commuting equilibrium, this study further develops the first-best time-dependent congestion tolling scheme to achieve the system optimum. Also, a location-dependent parking pricing scheme is developed to replace the tolling scheme. Furthermore, this study discusses the optimal parking supply to minimize the total system cost (including both the travel cost and the social cost of parking supply) under either user equilibrium or system optimum traffic flow pattern. It is found that the optimal planning of parking can be different from the non-AV situation, since the vehicles can drive themselves to parking spaces that are further away from the city center and walking of commuters is avoided. This paper sheds light on future parking supply planning and traffic management.     

Leveraging social networks for efficient hurricane evacuation

One of the important factors affecting evacuation performance is the departure time choices made by evacuees. Simultaneous departures of evacuees can lead to overloading of road networks causing congestion. We are especially interested in cases when evacuees subject to little or no risk of exposure evacuate along with evacuees subject to higher risk of threat (also known as shadow evacuation). One of the reasons for correlated evacuee departures is higher perceived risk of threat spread through social contacts. In this work, we study an evacuation scenario consisting of a high risk region and a surrounding low risk area. We propose a probabilistic evacuee departure time model incorporating both evacuee individual characteristics and the underlying evacuee social network. We find that the performance of an evacuation process can be improved by forcing a small subset of evacuees (inhibitors) in the low risk area to delay their departure. The performance of an evacuation is measured by both average travel time of the population and total evacuation time of the high risk evacuees. We derive closed form expressions for average travel time for ER random network. A detailed experimental analysis of various inhibitor selection strategies and their effectiveness on different social network topologies and risk distribution is performed. Results indicate that significant improvement in evacuation performance can be achieved in scenarios where evacuee social networks have short average path lengths and topologically influential evacuees do not belong to the high risk regions. Additionally, communities with stronger ties improve evacuation performance.     

Exploring the role of social capital influence variables on travel behaviour

This paper explores the potential role of individual trip characteristics and social capital network variables in the choice of transport mode. A sample of around 100 individuals living or working in one suburb of Madrid (i.e. Las Rosas district of Madrid) participated in a smartphone short panel survey, entering travel data for an entire working week. A Mixed Logit model was estimated with this data to analyze shifts to metro as a consequence of the opening of two new stations in the area. Apart from classical explanatory variables, such as travel time and cost, gender, license and car ownership, the model incorporated two “social capital network” variables: participation in voluntary activities and receiving help for various tasks (i.e. child care, housekeeping, etc.). Both variables improved the capacity of the model to explain transport mode shifts. Further, our results confirm that the shift towards metro was higher in the case of people “helped” and lower for those participating in some voluntary activities.     

Assessing social equity in distance based transit fares using a model of travel behavior

The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal/continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare distance travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe.     

Do social norms regarding carbon offsetting affect individual preferences towards this policy? Results from a stated choice experiment

This study investigates the idea that people’s willingness to offset flight-related carbon emissions is a function of the collective participation rate, which can be regarded as a social norm, towards carbon offsetting. Additionally, we reveal people’s preferences toward two other environmental policies; a baggage allowance and airline eco-efficiency index. A discrete choice experiment is designed and administrated among a sample of air travelers. The results indicate that carbon offsetting generates utility, with people gaining more utility when the collective participation rate is high. Additionally, it was found that the baggage allowance and the eco-efficiency index strongly influenced respondents’ airline choices. People also became more sensitivity towards a baggage allowance and the eco-efficiency label, when the collective offsetting rate was high.