Applying a structured simulation-based methodology to assess carpooling time–space potential

Abstract

Increasing urban traffic congestion calls for the study of alternative measures. One such measure is carpooling, a system in which a person shares his private vehicle with one or more people in a commuter trip. In principle, this system could lead to potentially significant reductions in the use of private vehicles; however, in practice it has achieved limited success. In this paper, we apply a simulation-based methodology that uses aggregated data from commuter trips in an urban area to create compatible and feasible random trips. These are then analyzed through a heuristic process recursively to find grouping possibilities, thus producing indicators of carpooling potential such as the percentage of matched trips. Using this methodology, simulations are run for the Lisbon Metropolitan Area (Portugal) and results show that an increase in the number of participants in a carpooling scheme will only increase the probability of matching up to a certain point, and that this probability varies significantly with time–space trip attributes.     

Seattle smart traveler: dynamic ridematching on the World Wide Web

Seattle Smart Traveler (SST) is an application of World Wide Web (WWW) technology to test the concept of automated dynamic rideshare matching. In SST, the rideshare clientele interact with the rideshare system using only WWW pages. SST collects spatial and temporal trip information using a series of WWW pages, performs a match using structured query language (SQL) specifications to a database engine, and supports both the standard phone-based contact methodology as well as two new, unique e-mail-based contact methodologies. SST was operated in parallel to a traditional, regional rideshare system for one year, and the two systems were marketed to the user community on a side-by-side basis. SST and the traditional system acquired approximately the same number of new users over a nine-month test period; however, there was little overlap in the population using the two parallel systems. SST demonstrates that there is a user population that can be reached using Internet technologies for immediate/dynamic ridematching that is not reached by traditional ridematch programs. This paper also reports a new statistical model for quantifying rideshare matching and carpooling. The model is validated using the SST experimental results, and the model demonstrates that the carpooling process is a quadratic function of the number of users participating.     

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.     

A literature review of flexible development of airport terminals

The Master Plan has long been the traditional go-to approach to airport development. It was originally conceived for a scenario of stable growth. In recent decades, however, the airport industry has undergone substantial structural changes, with the traditional Master Plan progressively revealing limitations with regard to airports coping with the market’s unpredictability. There have since been increased calls for flexibility in the approach as an alternative or as a complement to the traditional Master Plan. A flexible development plan helps to accommodate changes within terminals. This paper presents a review of the current literature on airport terminal flexibility, covering a total of 19 reference works. The works were analysed in terms of the concepts, definitions and deployment frameworks, or similar. The review reveals that research in airport terminal flexibility is still in its early stages. A consensual definition has yet to be defined and no robust framework for deploying flexibility has been defined. We propose a new definition of flexible development. Furthermore, flexibility has been studied essentially in the context of expansion. However, in certain regions where land availability is scarce, other forms of airport development may be more important. We conclude the paper with suggestions for future research areas.     

What encourages people to carpool? An evaluation of factors with meta-analysis

Non-household carpools (where two or more commuters from different residences travel together in the same private vehicle) bring public benefits. To encourage and incentivise it, transport practitioners and researchers must understand its private motivations and deterrents. Existing studies often report conflicting results or non-generalisable findings. Thus, a quantitative systematic review of the literature body is needed. Using meta-analysis, this study synthesised 22 existing empirical studies (representing over 79,000 observations) to produce an integrated review of the carpooling literature. The meta-analysis determined 24 non-household carpooling factors, and their effect sizes. Factors such as number of employees (\(\bar{r} = 0.42\)), partner matching programs (\(\bar{r} = 0.42\)), female (\(\bar{r} = 0.22\)) and fixed work schedule (\(\bar{r} = 0.15\)) were found to have strong effects on carpooling while judgmental factors (such as the motivation to save costs) only exhibited small influence (\(\bar{r} < 0.1\)). Based on the significant effects, the paper discussed prospects for improving carpooling uptake by developing: (i) target demographics, (ii) selling points for marketing, (iii) carpooling partner programs and (iv) multiple employer ‘super-pools’. The results warrant caution due to the small amount of studies synthesised. Transport practitioners might plan carpooling policies based on the findings; and transportation researchers might use the list of factors to model carpooling behaviour.     

Getting by with a little help from my friends…and family: immigrants and carpooling

While much of the scholarly literature on immigrants’ travel focuses on transit use, the newest arrivals to the United States make over twelve times as many trips by carpool as by transit. Using the 2001 National Household Travel Survey and multinomial logit mode choice models, we examine the determinants of carpooling. In particular, we focus on the likelihood of carpooling among immigrants—carpooling both within and across households. After controlling for relevant determinants of carpooling, we find that immigrants are far more likely to form household carpools than native-born adults and also are more likely than the native-born to form external carpools (outside the household). Moreover, when faced with the options of carpooling and public transit, immigrants—even recent arrivals—appear to prefer carpools over transit more strongly than the native born.     

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.     

A study of carpooling behaviour using a stated preference web survey in selected cities of India

This paper analyses how people perceive the idea of carpooling and evaluate preferences while making a decision to join a carpool. Analysing data from a web-based stated preference survey in India reveals that cognitive attitudes play a significant role in evaluating the perceived advantages and disadvantages of carpooling whereas intentions to carpool are associated with perceived negative evaluations. A factor analysis identifies two latent attitudinal factors: a ‘time–convenience’ factor that discourages carpooling and a ‘private–public cost’ factor that encourages carpooling. The study analyses the influential attributes – extra travel time, walking time to reach meeting point, waiting time at pickup point and cost savings – as explanatory variables for the utility of carpooling. Cost savings prove to be the most significant attribute when combined with other attributes, followed by extra travel time. The study provides the implications to policy-makers of designing promotional tools to improve the propensity of carpooling among single occupant vehicle drivers.     

The rise and fall of the American carpool: 1970–1990

Recent declines in carpooling among American commuters are analyzed using data derived from the US Census of Population, the Nationwide Personal Transportation Study, and the American Home Survey. The most important factors associated with recent declines in carpooling to and from work in the US include increasing household vehicle availability, falling real marginal fuel costs, and higher average educational attainments among commuters. Age, sex, family income, household lifecycle characteristics, urban form, racial diversity and relative poverty appear to have had smaller effects on observed changes in carpooling for the work trip.     

Shared ride services in North America: definitions,impacts, and the future of pooling

ABSTRACT

Shared ride services allow riders to share a ride to a common destination. They include ridesharing (carpooling and vanpooling); ridesplitting (a pooled version of ridesourcing/transportation network companies); taxi sharing; and microtransit. In recent years, growth of Internet-enabled wireless technologies, global satellite systems, and cloud computing - coupled with data sharing – are causing people to increase their use of mobile applications to share a ride. Some shared ride services, such as carpooling and vanpooling, can provide transportation, infrastructure, environmental, and social benefits. This paper reviews common shared ride service models, definitions, and summarises existing North American impact studies. Additionally, we explore the convergence of shared mobility; electrification; and automation, including the potential impacts of shared automated vehicle (SAV) systems. While SAV impacts remain uncertain, many practitioners and academic research predict higher efficiency, affordability, and lower greenhouse gas emissions. The impacts of SAVs will likely depend on the number of personally owned automated vehicles; types of sharing (concurrent or sequential); and the future modal split among public transit, shared fleets, and pooled rides. We conclude the paper with recommendations for local governments and public agencies to help in managing the transition to highly automated vehicles and encouraging higher occupancy modes.     

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.     

Attitudes,mode switching behavior,and the built environment: A longitudinal study in the Puget Sound Region

Carpooling in the US has a storied history. After experiencing a peak 20% mode share in 1980, the current share of carpooling for work trips is about 10% and the majority of these carpooling trips are made by intra-household members. Casting the choice between SOV and carpool as a social dilemma in which SOV is a noncooperative choice and carpool is a cooperative one, we propose to test two hypotheses. First, the switch from SOV to carpool and the reverse choice are attributed to different factors—structural factors, or those factors altering the objective features of a decision scenario such as travel time and travel cost, play a dominant role in the switch from carpool to SOV while psychosocial factors (attitudes and beliefs) play a critical role in the switch from SOV to carpool. Second, the two choices are underlay by different behavioral mechanisms. In particular, we expect self-justification by carpool-to-SOV switchers—after they switch from carpool to SOV, they adjusted their attitudes toward carpool accordingly to match their behavior. The analysis of the first three waves of the Puget Sound Transportation Panel supports these two hypotheses. Our study results recommend developing programs and policies that aim at influencing people’s subjective assessments of carpooling, in addition to the existing ones that mostly focus on incentivizing carpooling, and differentiating between programs seeking to encourage SOV users to switch to carpool and those aiming to maintain existing carpoolers.     

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.     

Modelling commuting mode choice with explicit consideration of carpool in the choice set formation

This article investigates the carpool mode choice option in the context of overall commuting mode choice preferences. The article uses a hybrid discrete choice modelling technique to jointly model the consideration of carpooling in the choice set formation as well as commuting mode choice together with the response bias corrections through the accommodation of measurement equations. A cross-nested error structure for the econometric formulation is used to capture correlations among various commuting modes and carpool consideration in the choice set. Empirical models are estimated using a data set collected through a week-long commuter survey in Edmonton, Alberta. The empirical model reveals many behavioural details of commuting mode choice and carpooling. Interestingly, it reveals that interactions between various Travel Demand Management (TDM) tools with the carpooling option can be different at different level of decision making (choice set formation level and final choice making level).     

Ridesharing in North America: Past,Present, and Future

Since the late 1990s, numerous ridematching programmes have integrated the Internet, mobile phones, and social networking into their services. Online ridematching systems are employing a range of new strategies to create “critical mass”: (1) regional and large employer partnerships, (2) financial incentives, (3) social networking to younger populations, and (4) real-time ridematching services that employ “smartphones” and automated ridematching software. Enhanced casual carpooling approaches, which focus on “meeting places”, are also being explored. Today, ridesharing represents approximately 8–11% of the transportation modal share in Canada and the USA, respectively. There are approximately 638 ridematching programmes in North America. Ridesharing's evolution can be categorized into five phases: (1) World War II car-sharing (or carpooling) clubs; (2) major responses to the 1970s energy crises; (3) early organized ridesharing schemes; (4) reliable ridesharing systems; and (5) technology-enabled ridematching. While ridesharing's future growth and direction are uncertain, the next decade is likely to include greater interoperability among services, technology integration, and stronger policy support. In light of growing concerns about climate change, congestion, and oil dependency, more research is needed to better understand ridesharing's impacts on infrastructure, congestion, and energy/emissions.     

Commuter ride-sharing using topology-based vehicle trajectory clustering: Methodology,application and impact evaluation

This paper illustrates a ride matching method for commuting trips based on clustering trajectories, and a modeling and simulation framework with ride-sharing behaviors to illustrate its potential impact. It proposes data mining solutions to reduce traffic demand and encourage more environment-friendly behaviors. The main contribution is a new data-driven ride-matching method, which tracks personal preferences of road choices and travel patterns to identify potential ride-sharing routes for carpool commuters. Compared with prevalent carpooling algorithms, which allow users to enter departure and destination information for on-demand trips, the proposed method focuses more on regular commuting trips. The potential effectiveness of the approach is evaluated using a traffic simulation-assignment framework with ride-sharing participation using the routes suggested by our algorithm. Two types of ride-sharing participation scenarios, with and without carpooling information, are considered. A case study with the Chicago tested is conducted to demonstrate the proposed framework’s ability to support better decision-making for carpool commuters. The results indicate that with ride-matching recommendations using shared vehicle trajectory data, carpool programs for commuters contribute to a less congested traffic state and environment-friendly travel patterns.     

Modelling public transport on‐board congestion: comparing schedule‐based and agent‐based assignment approaches and their implications

Transit systems are subject to congestion that influences system performance and level of service. The evaluation of measures to relieve congestion requires models that can capture their network effects and passengers' adaptation. In particular, on‐board congestion leads to an increase of crowding discomfort and denied boarding and a decrease in service reliability. This study performs a systematic comparison of alternative approaches to modelling on‐board congestion in transit networks. In particular, the congestion‐related functionalities of a schedule‐based model and an agent‐based transit assignment model are investigated, by comparing VISUM and BusMezzo, respectively. The theoretical background, modelling principles and implementation details of the alternative models are examined and demonstrated by testing various operational scenarios for an example network. The results suggest that differences in modelling passenger arrival process, choice‐set generation and route choice model yield systematically different passenger loads. The schedule‐based model is insensitive to a uniform increase in demand or decrease in capacity when caused by either vehicle capacity or service frequency reduction. In contrast, nominal travel times increase in the agent‐based model as demand increases or capacity decreases. The marginal increase in travel time increases as the network becomes more saturated. Whilst none of the existing models capture the full range of congestion effects and related behavioural responses, existing models can support different planning decisions. Copyright © 2016 John Wiley & Sons, Ltd.     

Working from 9 to 6? An analysis of in-home and out-of-home working schedules

The widespread adoption of information and communication technologies (ICT) and flexible work arrangements have enabled people to schedule their work activities in more flexible ways. New forms of working have already emerged. However, research about flexible work schedules is still scarce. The aim of this study is to determine the factors impacting the propensity to adopt a flexible work schedule, and establish how and to what extent the timing of work is influenced by ICTs, work, and sociodemographic characteristics. The analysis suggests that work-related characteristics and ICT usage seem to be more important for work-schedule flexibility than sociodemographic characteristics are.     

Impact of high occupancy vehicle lanes on carpooling behavior

High occupancy vehicle lanes have become an integral part of regional transportation planning. Their purpose is to increase ridesharing by offering a travel time advantage to multiple occupant vehicles. This paper examines the extent to which an HOV facility increases ridesharing. Using data from the Route 55 HOV facility in Orange Country, California, changes in the carpooling rate on Route 55 are compared to that of a control group of freeway commuters. The analysis shows that the carpooling rate among peak period commuters, and particularly those who use the entire length of the facility, has increased. However, there has been no significant increase in ridesharing among the entire population of Route 55 commuters. Results suggest that barriers to increased ridesharing are formidable, that travel time savings must be large in order to attract new carpoolers, and that further increases in capooling will likely require development of extensive HOV lane systems.     

Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility

In the past decade, many studies have explored the relationship between travelers’ travel mode and their trip satisfaction. Various characteristics of the chosen travel modes have been found to influence trip experiences; however, apart from the chosen modes, travelers’ variability in mode use and their ability to vary have not been investigated in the trip satisfaction literature. This current paper presents an analysis of commuting trip satisfaction in Beijing with a particular focus on the influence of commuters’ multimodal behavior on multiple workdays and their modal flexibility for each commuting trip. Consistent with previous studies, we find that commuting trips by active modes are the most satisfying, followed by trips by car and public transport. In Beijing, public transport dominates. Urban residents increasingly acquire automobiles, but a strict vehicle policy has been implemented to restrict the use of private cars on workdays. In this comparatively constrained context for transport mode choice, we find a significant portion of commuters showing multimodal behavior. We also find that multimodal commuters tend to feel less satisfied with trips by alternative modes compared with monomodal commuters, which is probably related to their undesirable deviation from habitual transport modes. Furthermore, the relationship between modal flexibility and trip satisfaction is not linear, but U-shaped. Commuters with high flexibility are generally most satisfied because there is a higher possibility for them to choose their mode of transport out of preference. Very inflexible commuters can also reach a relatively high satisfaction level, however, which is probably caused by their lower expectations beforehand and the fact that they did not have an alternative to regret in trip satisfaction assessments.