Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling?

Carpooling is an effective solution to major environmental problems but it is insufficiently used, particularly in France. In order to increase carpooling, it is important to understand why people do or not carpool. This study, carried out a large sample of drivers, was aimed at (1) identifying whether or not people carpool, (2) investigating the factual data (e.g. socio-demographics, transportation accessibility) and motivational factors (e.g. attitudes regarding car use, public transportation, environment) upon which carpoolers and non-carpoolers differ, and (3) highlighting the main determinants of the practice of carpooling. An online survey was administered to 1207 French drivers (48% men, ages 19–87) recruited by means of a random-quota method. In our sample, 52.5% of the participants said they were using carpooling. Carpoolers and non-carpoolers were similar in terms of kilometers driven per year, distance to their workplace or schools/universities, and public transportation accessibility. Compared to non-carpoolers, carpoolers were more likely to be women, have children, have positive attitudes toward public transport, and be more environmentally aware. These results suggest various different strategies for increasing the number of carpoolers and the frequency of carpooling use by taking into account the gender of road users.     

Studying the benefits of carpooling in an urban area using automatic vehicle identification data

Carpooling has been considered a solution for alleviating traffic congestion and reducing air pollution in cities. However, the quantification of the benefits of large-scale carpooling in urban areas remains a challenge due to insufficient travel trajectory data. In this study, a trajectory reconstruction method is proposed to capture vehicle trajectories based on citywide license plate recognition (LPR) data. Then, the prospects of large-scale carpooling in an urban area under two scenarios, namely, all vehicle travel demands under real-time carpooling condition and commuter vehicle travel demands under long-term carpooling condition, are evaluated by solving an integer programming model based on an updated longest common subsequence (LCS) algorithm. A maximum weight non-bipartite matching algorithm is introduced to find the optimal solution for the proposed model. Finally, road network trip volume reduction and travel speed improvement are estimated to measure the traffic benefits attributed to carpooling. This study is applied to a dataset that contains millions of LPR data recorded in Langfang, China for 1 week. Results demonstrate that under the real-time carpooling condition, the total trip volumes for different carpooling comfort levels decrease by 32–49%, and the peak-hour travel speeds on most road segments increase by 5–40%. The long-term carpooling relationship among commuter vehicles can reduce commuter trips by an average of 30% and 24% in the morning and evening peak hours, respectively, during workdays. This study shows the application potential and promotes the development of this vehicle travel mode.     

Carpooling and carpool clubs: Clarifying concepts and assessing value enhancement possibilities through a Stated Preference web survey in Lisbon, Portugal

The increase of urban traffic congestion calls for studying alternative measures for mobility management, and one of these measures is carpooling. In theory, these systems could lead to great reductions in the use of private vehicles; however, in practice they have obtained limited success for two main reasons: the psychological barriers associated with riding with strangers and poor schedule flexibility. To overcome some of the limitations of the traditional schemes, we proposed studying a carpooling club model with two main new features: establishing a base trust level for carpoolers to find compatible matches for traditional groups and at the same time allowing to search for a ride in an alternative group when the pool member has a trip schedule different from the usual one. A web-based survey was developed for the Lisbon Metropolitan Region (Portugal), including a Stated Preference experiment, to test the concept and confirm previous knowledge on these systems’ determinants. It was found through a binary logit Discrete Choice Model calibration that carpooling is still attached with lower income strata and that saving money is still an important reason for participating in it. The club itself does not show promise introducing more flexibility in these systems; however, it should provide a way for persons to interact and trust each other at least to the level of working colleagues.     

Quantifying the environmental impacts of increasing high occupancy vehicle lanes in the United States

To what extent will increasing High Occupancy Vehicle (HOV) lane-kilometer incentivize carpooling and reduce emissions of air pollutants and greenhouse gases? To answer these questions, we develop a multiple regression model relating HOV lanes and other socioeconomic factors to carpooling propensity in all 50 U.S. states and the District of Columbia, then calculate the extent to which increasing HOV lane-kilometers would lead to reductions in carbon dioxide equivalent (CO₂e) and major air pollutants across the U.S., by state. Increasing HOV lane extent has the greatest potential to reduce annual CO₂e in the District of Columbia, followed by Hawaii and New York. The smallest potential is found in states with the lowest population density, led by North Dakota. We then explore the extent to which recommendations made at one level of data aggregation (that of individual states) may be valid for another level, such as individual counties. The only state with sufficient data available to disaggregate the model to the county level is California, where we found a lower potential for state-wide CO₂e emission reductions under the county-level model as compared to the state-level model (0.69% as compared to 1.08%, under the same hypothetical scenario), albeit with significant differences in emission reduction potential between counties with higher vs. lower population densities. This analysis demonstrates the potential to generate generalizable insight into the magnitude of vehicle emission reductions that might be achieved through expanding HOV lanes, and highlights the importance of data disaggregation in identifying the optimal locations for potential reductions.     

Social networks as a source of private-vehicle transportation: The practice of getting rides and borrowing vehicles among Mexican immigrants in California

We examine the role of social networks in enabling access to private-vehicle transportation, through getting rides and borrowing cars. Based on qualitative findings from ten focus group discussions with recent Mexican immigrants to California, half of whom have no car, we describe the extent to which participants depend on rides and borrowed cars for transportation. We highlight the unique aspects of informal access to cars, drawing on social exchange theory and related research to characterize the procurement process and likely levels of exchange. We discuss the implications of these findings for transportation services that might serve this and other community groups.     

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.     

Of “white crows” and “cash savers:” A qualitative study of travel behavior and perceptions of ridesharing in Denmark

Based on original research collected through semi-structured research interviews and five focus groups throughout Denmark, this study explores Danish perceptions about ad hoc, acquaintance-based, and organization-based ridesharing (“carpooling”). Using a grounded, qualitative factor analysis approach, it investigates the elements that influence the adoption (and non-adoption) of ridesharing and identifies market segments and business models that may enable planners to overcome existing barriers. The article finds that Danish drivers and commuters appear to be split on the topic. Negative perceptions reported by respondents include lack of availability and difficulty finding rides, viewing ridesharing as unsafe or unsecure, and expectations of social awkwardness, among others. Positive perceptions reported include cost savings compared to public and private transport, greater flexibility of travel times, and the ability to socialize with vehicle occupants. These contrasting views lead us to conclude that existing theories and models of ridesharing behavior may need to be fundamentally rethought, both in Denmark and possibly elsewhere. Our results also suggest that ridesharing efforts framed around climate change or environmental sustainability will not likely be successful in Denmark.     

Multi-agent simulation of individual mobility behavior in carpooling

Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely; (i) create a motive to carpool, (ii) communicate this motive with other agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans, and (v) provide a feedback to all concerned agents. In this paper, we present a conceptual design of an agent-based model (ABM) for the carpooling a that serves as a proof of concept. Our model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our carpooling application, we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm, and a utility function to trigger the negotiation process between agents. We developed a prototype of our agent-based carpooling application based on the work presented in this paper and carried out a validation study of our results with real data collected in Flanders, Belgium.     

Optimal starting location of an HOV lane for a linear monocentric urban area

This paper analyzes the optimal starting location of a high-occupancy-vehicle (HOV) lane for a linear monocentric urban area. Both travel time and carpooling costs are taken into account. The research proposes an analytical framework for the case with a continuum demand distribution along a highway corridor. The objective is assumed to maximize social welfare of the transportation system, which is the difference between the total user benefit and travel cost. Numerical analysis via simulation experiments was conducted to seek the existence of an optimal solution. Based on the results of a sensitivity analysis, we find a specific relationship between the carpooling cost and the optimal design of the starting point of an HOV lane.     

Hybrid cars and HOV lanes

The 2005 California Clean Air Access Sticker program offered stickers to owners of hybrid cars allowing unrestricted access to High Occupancy Vehicle lanes. The program was conceived as a zero-cost mechanism to encourage purchase of hybrid cars and to reduce air pollution. Information from sales of used hybrids allows us to estimate sticker market value. We then derive the value of excess HOV space the hybrids occupied, which is considerably greater than the air pollution benefits achieved. A more effective policy would sell space to drivers of any vehicle and use the revenue to stimulate hybrid demand, preferably via direct subsidy.