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Never Drive Alone: Boosting Carpooling with Network Analysis
Abstract
Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.
... Ride sharing is a very useful way of traveling both in inter and intra city. According to scientific research, traffic has several adverse effects on the environment, our health and quality of life, economy and our whole societies (Guidotti et al. 2017). At the same time carpooling is an eco-friendly and sustainable alternative for travelers in order to save time, costs such as fuel, toll and parking costs, reduce emission and traffic congestion (Galland et al. 2014). ...
... According to Guidotti et al. (2017), one way to improve the use of carpooling is being aware of people's travel behavior. Having sufficient data, which is called mobility data, such as the origin and the destination of travelers, a network of potential carpooling can be constructed, which will make it possible to match the traveler's trips. ...
... 150,000 cars were equipped with GPS trajectories for a month in Tuscany, the region of central Italy with Florence and Pisa. Different investigations in carpooling revealed that the method that analyzes network and node rankings has the best performance theoretically, since it dramatically diminishes the number of single occupancy vehicles (Guidotti et al. 2017). Knowledge about systematic behavior, and the measures regarding carpooling, could really help our everyday life in reducing traffic, saving money and producing less pollution (Galland et al. 2014). ...
... This analysis was conducted by means of identifying the patterned themes from the literature obtained. Generally, the result of analysis can be seen in the [18], [19], [20], [21], [22], [23] , [24] , [25] , [26], [27], [28], [2], [1], [29], [30], [31], [32] Reducing Cost 9 (45%) [19], [33], [34], [20], [22], [30], [26], [28], [1] Reducing Congestion 15 (75%) [18], [19], [34], [20] , [23] , [30], [25] [26], [27], [28], [2], [1], [29], [24], [32] Efficiency of parking area 1 (5%) [34] Reducing Fuel consumption 8 (40%) [1], [34], [21], [30], [35], [1], [29], [24] Save time 2 (10%) [33], [27] Another finding shows that only one study that discuss the importance of government policy in the relations between taxi online and green sustainable concept. It indicates the researchers' low interest in studying this area of research. ...
... This analysis was conducted by means of identifying the patterned themes from the literature obtained. Generally, the result of analysis can be seen in the [18], [19], [20], [21], [22], [23] , [24] , [25] , [26], [27], [28], [2], [1], [29], [30], [31], [32] Reducing Cost 9 (45%) [19], [33], [34], [20], [22], [30], [26], [28], [1] Reducing Congestion 15 (75%) [18], [19], [34], [20] , [23] , [30], [25] [26], [27], [28], [2], [1], [29], [24], [32] Efficiency of parking area 1 (5%) [34] Reducing Fuel consumption 8 (40%) [1], [34], [21], [30], [35], [1], [29], [24] Save time 2 (10%) [33], [27] Another finding shows that only one study that discuss the importance of government policy in the relations between taxi online and green sustainable concept. It indicates the researchers' low interest in studying this area of research. ...
... This analysis was conducted by means of identifying the patterned themes from the literature obtained. Generally, the result of analysis can be seen in the [18], [19], [20], [21], [22], [23] , [24] , [25] , [26], [27], [28], [2], [1], [29], [30], [31], [32] Reducing Cost 9 (45%) [19], [33], [34], [20], [22], [30], [26], [28], [1] Reducing Congestion 15 (75%) [18], [19], [34], [20] , [23] , [30], [25] [26], [27], [28], [2], [1], [29], [24], [32] Efficiency of parking area 1 (5%) [34] Reducing Fuel consumption 8 (40%) [1], [34], [21], [30], [35], [1], [29], [24] Save time 2 (10%) [33], [27] Another finding shows that only one study that discuss the importance of government policy in the relations between taxi online and green sustainable concept. It indicates the researchers' low interest in studying this area of research. ...
Online taxi is a transportation mode that mostly grows in urban area. It integrates online technology from cellular phone application to offline activities in its service. This transportation mode is an example of ride sharing concept. This transportation emergence has generated positive and negative impact, where many studies had discussed that. Any kind of transportation mode has an impact to the environment. However the online taxi impact on the environment has rarely been heard. This article aimed to identify the recent studies on the relation of online taxi with green concept. Using Systematic Literature Review and thematic analysis method, the result of research showed that many studies mentioned online taxi is closely related to the green concept. In addition to that, there are still limited number of studies conducted on the policy connecting online taxi to green sustainable energy. Discussion and implication of research was discussed further in the article.
... These digital traces take note of our mobility behavior with extraordinary precision and can be exploited for realizing the most disparate location-based services [1]: recommender systems [2], [3], personalized journey planners [4], [5], carpooling systems [6], [7], etc. Such services are based on the predictability of human behavior: typically every individual systematically repeats a small set of actions [8] such as visiting a limited number of places [9]. ...
... The loop (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] reproduces A by iteratively adding the predicted/simulated positions for each regular interval τ (line 19). The number of iterations depends on the time tick τ . ...
... If that is the case, function isInLoc returns Algorithm 2: reproduceAgenda(P u , d, τ ) 1 A ← ∅; not moving ← T rue; 2 Lcur ← initLoc(ω, ρ, Q u , d); 3 for ts in 0, τ, 2τ, 3τ, . . . , Υ do 4 if not moving then 5 not moving ← isInLoc(ρ, Lcur, ts, d); 6 if not moving then 7 lon, lat ← getP osition(Q u , Lcur, d); true and the position of the location point of L cur is extracted (line 7). Otherwise, the user will leave L cur to reach L nxt , which is chosen by function nextLoc according to π (line 9). ...
The avalanche of mobility data like GPS and GSM daily produced by each user through mobile devices enables personalized mobility-services improving everyday life. The base for these mobility-services lies in the predictability of human behavior. In this paper we propose an approach for reproducing the user's personal mobility agenda that is able to predict the user's positions for the whole day. We reproduce the agenda by exploiting a data-driven personal mobility model able to capture and summarize different aspects of the systematic mobility behavior of a user. We show how the proposed approach outperforms typical methodologies adopted in the literature on four different real GPS datasets. Moreover, we analyze some features of the mobility models and we discuss how they can be employed as agents of a simulator for what-if mobility analysis.
... Multi-agent approach for achieving a decentralized but parallel process to handle network's subdivision [181] RideMyRoute Data quality Open source data, Consistency and compatibility of data, Protocols for data exchange of carpool data [65] Analyzing the potentiality of a carpooling service ...
... Whereas, in [181] authors have introduced a new travel planning app called Ride My Route that enables customers to ind and make trips related to carpooling and public transportation, showcasing the highlights of its design, development, and testing. Guidotti et al. [65] examined the expected efect of carpooling as an aggregate marvel arising from public recognition through network insights and built a network of potential carpooling. They proposed a novel approach to evaluate the carpooling service eiciency and suggest an appointment with regular car drivers to avoid driving alone. ...
Owing to the advancements in communication and computation technologies, the dream of commercialized connected and autonomous cars is becoming a reality. However, among other challenges such as environmental pollution, cost, maintenance, security, and privacy, the ownership of vehicles (especially for Autonomous Vehicles (AV)) is the major obstacle in the realization of this technology at the commercial level. Furthermore, the business model of pay-as-you-go type services further attracts the consumer because there is no need for upfront investment. In this vein, the idea of car-sharing ( aka carpooling) is getting ground due to, at least in part, its simplicity, cost-effectiveness, and affordable choice of transportation. Carpooling systems are still in their infancy and face challenges such as scheduling, matching passengers interests, business model, security, privacy, and communication. To date, a plethora of research work has already been done covering different aspects of carpooling services (ranging from applications to communication and technologies); however, there is still a lack of a holistic, comprehensive survey that can be a one-stop-shop for the researchers in this area to, i) find all the relevant information, and ii) identify the future research directions. To fill these research challenges, this paper provides a comprehensive survey on carpooling in autonomous and connected vehicles and covers architecture, components, and solutions, including scheduling, matching, mobility, pricing models of carpooling. We also discuss the current challenges in carpooling and identify future research directions. This survey is aimed to spur further discussion among the research community for the effective realization of carpooling.
... It is easy to understand the potentiality of data science by introducing terms such as urban planning, public transportation, reduction of energy consumption, ecological sustainability, safety, and management of mass events. These terms represent only the front line of topics that can benefit from the awareness that big data might provide to the city stakeholders [22,27,29]. Several methods allowing human mobility analysis and prediction are available in the literature: MyWay [47] exploits individual systematic behaviors to predict future human movements by combining individual and collective learned models. ...
... Several methods allowing human mobility analysis and prediction are available in the literature: MyWay [47] exploits individual systematic behaviors to predict future human movements by combining individual and collective learned models. Carpooling [22] is based on mobility data from travelers in a given territory and constructs a network of potential carpooling users, by exploiting topological properties, highlighting sub-populations with higher chances to create a carpooling community and the propensity of users to be either drivers or passengers in a shared car. Event attendance prediction [13] analyzes users' call habits and classifies people into behavioral categories, dividing them among residents, commuters, and visitors and allows to observe the variety of behaviors of city users and the attendance in big events in cities. ...
This paper shows data science’s potential for disruptive innovation in science, industry, policy, and people’s lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.
... Recently, however, the scientiőc community has increasingly adopted the term łmobility dataž and is advocating for the term łMobility Data Sciencež, alongside other standard terms from the data science pipeline, to foster a cohesive approach to the comprehensive study of mobility data [2,3]. This paper focuses on Mobility Data Analysis (MDA), which refers to the process of analyzing mobility data to extract patterns [4], understand movement behaviors [5,6], and support applications such as urban planning [7], transportation optimization [8], and location-based services [9]. ...
Although Mobility Data Analysis (MDA) has been explored for a long time, it still lags behind advancements in other fields.A common issue in MDA is the lack of methods' standardization and reusability. On the other hand, for instance, in time series analysis, the existing methods are typically general-purpose, and it is possible to apply them across diverse datasets and applications without extensive customization. Still, in MDA, most contributions are ad-hoc and designed to address specific research questions, which limits their generalizability and reusability. Recently, some researchers explored the application of shapelet transform to trajectory data, i.e., extracting discriminatory sub-trajectories from training data to be used as classification features. Unlike current MDA methods, this line of research eliminates the need for feature engineering, greatly improving its ability to generalize. While shapelets on mobility data have shown state-of-the-art performance on public classification datasets, it is still not clear why they work. Are these subtrajectories merely proxies for geographic location, or do they also capture motion dynamics? We empirically show that shapelet-based approaches are a viable alternative to classical methods and flexible enough to solve MDA tasks related solely to trajectory shape, solely to movement dynamics, and those related to both. Additionally, we investigate the problem of Geographic Transferability, showing that such approaches offer a promising starting point for tackling this challenge.
... The authors in [17] develops a model for the carpooling problem that incorporates pre-matching information (e.g., previous accepted passengers). Network analytics is used in [18] to determine subpopulations of travellers in a given territory with a higher change to create a carpooling community, and the predisposition of users to be either drivers or passengers in a shared car. A measure of enjoyability for a carpooling ride is defined in [7] based on social similarities between any two users and tendency of a person to group with similar ones. ...
Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules. A number of car pooling matching services have been designed in order to efficiently find successful ride matches in a given pool of drivers and potential passengers. However, it is now recognised that many non-monetary aspects and social considerations, besides simple mobility needs, may influence the individual willingness of sharing a ride, which are difficult to predict. To address this problem, in this study we propose GoTogether, a recommender system for car pooling services that leverages on learning-to-rank techniques to automatically derive the personalised ranking model of each user from the history of her choices (i.e., the type of accepted or rejected shared rides). Then, GoTogether builds the list of recommended rides in order to maximise the success rate of the offered matches. To test the performance of our scheme we use real data from Twitter and Foursquare sources in order to generate a dataset of plausible mobility patterns and ride requests in a metropolitan area. The results show that the proposed solution quickly obtain an accurate prediction of the personalised user's choice model both in static and dynamic conditions.
... Carpooling (i.e., the act in which two or more travelers share the same car for a common trip) is one of the possibilities brought forward to reduce traffic and its externalities [1]. In some countries, the demand for carpooling fluctuates with oil prices and energy sources, while in China, the traditional method of carpooling only occurs between neighborhoods in the early stage of carpooling development, and few people choose this mode. ...
This paper firstly analyzes the comparative advantages of carpooling under the mobile Internet and traditional travel modes, including buses, private cars and taxis, as well as the differences between carpooling under the mobile Internet and traditional carpooling, so as to obtain the factors that affect travelers’ mode choices. Secondly, the mixed logit model is used to describe the travelers’ travel mode choice behavior, which effectively avoids the limitations of the IIA characteristics and preference randomness of the logit model. Finally, we conducted an SP survey on 1077 samples online and offline. After eliminating some invalid samples, 984 valid ones were obtained. Based on these survey data, we analyze the impacts of carpooling under the mobile Internet on the mode shares of traditional travel modes. The results show that for different trip lengths, carpooling under the mobile Internet has different degrees of substitution for buses, taxis and private cars. That is to say, travelers who previously chose buses and other modes will shift to carpooling due to the mobile Internet. In addition, in most cases, the emergence of carpooling under the mobile Internet is helpful to reduce the traffic volume in the urban road network, thus alleviating the urban congestion. However, when the trip length is short and the seat utilization ratio of carpooling under the mobile Internet is low, carpooling under the mobile Internet will increase the traffic volume.
... Guidotti et al. [45] 2017 EU Carpooling is the act where two or more travellers share the same car for a common trip Kladeftiras and Antoniou [55] 2015 EU (Greece) Dynamic ride-sharing and traditional carpooling both involve pre-arrangements, but dynamic ride-sharing differs in the fact that the scheduling of the trip occurs in a case-by-case basis Lee and Savelsbergh [57] 2015 US Dynamic ride-sharing is a recent alternative in which people with similar travel plans are matched and travel together. Ride-sharing systems, where participants with similar travel itineraries are paired together Nourinejad and Roorda [71] 2016 Canada Dynamic ride-sharing involves a service provider that matches potential drivers and passengers with similar itineraries allowing them to travel together and share the costs. ...
Ride-sharing is an innovative on-demand transport service that aims to promote sustainable transport, reduce car utilization, increase vehicle occupancy and public transport ridership. By reviewing ride-sharing studies around the world, this paper aims to map major aspects of ride-sharing, including online platforms, user factors and barriers that affect ride-sharing services, and extract useful insights regarding their successful implementation.
A systematic literature review is conducted on scientific publications in English language. Articles are eligible if they report a study on user factors affecting ride-sharing use and/or barriers preventing ride-sharing implementation; ride-sharing online platforms in these articles are also recorded and are further explored through their official websites. A database is built that organizes articles per author, year and location, summarizes online platform attributes, and groups user factors associated with the likelihood to ride-share.
The review shows that the term “ride-sharing” is used in the literature for both profit and non-profit ride-sharing services. In total, twenty-nine ride-sharing online platforms are recorded and analyzed according to specific characteristics. Sixteen user factors related to the likelihood to ride-share are recorded and grouped into sociodemographic, location and system factors. While location and system factors are found to follow a pattern among studies, mixed findings are recorded on the relationship between sociodemographic factors and ride-sharing. Factors that may hinder the development of ride-sharing systems are grouped into economic, technological, business, behavioral and regulatory barriers.
Opportunities exist to improve the quality of existing ride-sharing services and plan successful new ones. Future research efforts should focus towards studying ride-sharing users' trip purpose (i.e., work, university, shopping, etc.), investigating factors associated to ride-sharing before and after implementation of the service, and perform cross-case studies between cities and countries of the same continent to compare findings.
... In mobility analytics one of the fundamental concepts is movement, namely the part of mobility data that describes a transfer from one place where the individual (or the object) was staying, to another one were the user will stop. Identifying movements in the raw stream of positions, for instance the continuous flow of GPS traces of a vehicle, is essential to many tasks, as it enables the development of mobility data models [19,38] and applications like carpooling [2,17], trajectory prediction [45] and car crash prediction [16], which are based on stop locations and the transitions between them. Errors in identifying stops and movements greatly affect the results of modeling, and therefore the overall performances. ...
Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, user-adaptive and essentially parameter-free solution that flexibly adjusts the segmentation criteria to the specific user under study and to the geographical areas they traverse. Experiments over real data, and comparison against simple and state-of-the-art competitors show that the flexibility of the proposed methods has a positive impact on results.
... Viajar de forma compartida es la segunda manera más popular de desplazamiento, y tal vez una de las menos entendida (Guidotti et al., 2017). ...
En este estudio se investiga la percepción del riesgo y el contagio en red en el consumo de viajes compartidos — Uber y Carpooling—. El universo del estudio se relaciona directamente con los mercados impactados por la «Ubereconomía», principalmente, los taxis y los demás medios de transporte urbano. La hipótesis central de este estudio propone que los riesgos se diseminan a partir de una relación interpersonal entre participantes de una comunidad y el hecho de que ese riesgo puede ser propagado por un primer actor. La brecha explorada en este estudio contempla el contagio de la percepción del riesgo en economías de acceso para viajes compartidos y su impacto en el mercado de taxis. Los descubrimientos de la investigación indican que la seguridad y el confort son los principales aspectos del consumo de viajes compartidos. También se indican fallas en el sistema de evaluación de los usuarios y de los prestadores de servicio al realizar la validación del perfil a partir de redes sociales como Facebook y otros. El análisis permite concluir que, dado que la seguridad y la comodidad son atributos clave, cabe desarrollar funciones en las plataformas que tengan como objetivo mejorar los aplicativos, principalmente, en la de evaluación de los involucrados y en los indicadores de mantenimiento de los vehículos. Finalmente se establece la posibilidad extender los hallazgos de esta investigación para los otros negocios realizados en plataformas digitales de economía compartida.
... Whilst GPS traces are a rich source of information of driver behaviour, they are voluminous and complex. Our approach is based on network analysis tools [Guidotti et al., 2017] and complexity reduction/harmonisation algorithms [Douglas and Peucker, 2011]. This topological simplification is essential to be able to mutualise GPS traces which share common arrival times at the carpooling meeting points. ...
Carpooling has the potential to transform itself into a mass transportation mode by abandoning its adherence to deterministic passenger-driver matching for door-to-door journeys, and by adopting instead stochastic matching on a network of fixed meeting points. Stochastic matching is where a passenger sends out a carpooling request at a meeting point, and then waits for the arrival of a self-selected driver who is already travelling to the requested meeting point. Crucially there is no centrally dispatched driver. Moreover, the carpooling is assured only between the meeting points, so the onus is on the passengers to travel to/from them by their own means. Thus the success of a stochastic carpooling service relies on the convergence, with minimal perturbation to their existing travel patterns, to the meeting points which are highly frequented by both passengers and drivers. Due to the innovative nature of stochastic carpooling, existing off-the-shelf workflows are largely insufficient for this purpose. To fill the gap in the market, we introduce a novel workflow, comprising of a combination of data science and GIS (Geographic Information Systems), to analyse driver GPS traces. We implement it for an operational stochastic carpooling service in south-eastern France, and we demonstrate that relaxing door-to-door matching reduces passenger waiting times. Our workflow provides additional key operational indicators, namely the driver flow maps, the driver flow temporal profiles and the driver participation rates.
... Whilst GPS traces are a rich source of information of driver behaviour, they are voluminous and complex. Our approach is based on network analysis tools [Guidotti et al., 2017] and complexity reduction/harmonisation algorithms [Douglas and Peucker, 2011]. This topological simplification is essential to be able to mutualise GPS traces which share common arrival times at the carpooling meeting points. ...
Carpooling has the potential to transform itself into a mass transportation mode by abandoning its adherence to deterministic passenger-driver matching for door-to-door journeys, and by adopting instead stochastic matching on a network of fixed meeting points. Stochastic matching is where a passenger sends out a carpooling request at a meeting point, and then waits for the arrival of a self-selected driver who is already travelling to the requested meeting point. Crucially there is no centrally dispatched driver. Moreover, the carpooling is assured only between the meeting points, so the onus is on the passengers to travel to/from them by their own means. Thus the success of a stochastic carpooling service relies on the convergence , with minimal perturbation to their existing travel patterns, to the meeting points which are highly frequented by both passengers and drivers. Due to the innovative nature of stochastic carpooling, existing off-the-shelf workflows are largely insufficient for this purpose. To fill the gap in the market, we introduce a novel workflow, comprising of a combination of data science and GIS (Geographic Information Systems), to analyse driver GPS traces. We implement it for an operational stochastic carpooling service in southeastern France, and we demonstrate that relaxing door-to-door matching reduces passenger waiting times. Our workflow provides additional key operational indicators, namely the driver flow maps, the driver flow temporal profiles and the driver participation rates.
... It consists of making available to travelers places in private or business cars. In the beginning, the system appeared where there was no public transport due to the low traffic volume [35,36]. ...
The work on the impact of innovative solutions in urban transport on the inhabitants’ quality of life was discussed. This paper presents the characteristics of the use of shared vehicles in the agglomeration, based on the example of the Tri-City. An analysis of vehicles’ use in given periods of time was performed, indicating the growing interest in using this type of transport in the city. The work was divided into four chapters. The first part concerns the history of travel and urban development. The second discusses all currently available communication solutions in cities. The third chapter contains the research part. It focuses on presenting changes in vehicle availability over a more extended period. The fourth chapter describes the functioning of cars “for minutes” and the operation and use of dedicated mobile applications. The work ended with a summary of theoretical and cognitive content. A significant contribution is a brief analysis of the shared car market in the Tri-City. The available options are characterized. Also, the degree of use has been analyzed. The study concluded with theses about the further rapid development of this industry in northern Poland.
... GIS can reveal hidden information, analyze the data from different perspectives and summarize them into useful information, extract spatial patterns, analyze spatially relationship, identify problematic areas and recommend possible solutions based on user-generated data in a social networking context. This tool could perform a wide range of spatial analyses including distance, overlay, and route-finding analyses based on trip information, such as time, origin, destination, trip, and detour distance ranges to provide a number of location-based services including the identification of potential travelers and optimum routes for traveling (Sui and Goodchild 2011;Guidotti et al. 2017;Bachmann et al. 2018). ...
The increasing use of private cars in large cities is accompanied by adverse ramifications such as severe shortage of parking spaces, traffic congestion, air pollution, a high level of fuel consumption, and travel cost. Ridesharing is one of the emerging solutions that facilitate the simultaneous match of drivers and passengers with similar travel schedules. In this paper, ridesharing equals carsharing which involves a cooperative trip of at least two passengers who share an automobile and must match their itineraries. The main objective of this paper is to develop a ridesharing system based on the geosocial network to be employed in Tehran, capital of Iran. In this regard, a new hybrid approach based on GIS and ant colony is developed to provide optimal shared-routes through integrating three main procedures sequentially. First, the spatio-temporal clustering of passengers is carried out using the K-means algorithm, second spatio-temporal matching of passengers ‘clusters, and drivers’ has been carried out by combining Voronoi continuous range query (VCRQ), a region connected calculus (RCC5) and Allen’s temporal interval algebra. Third, the optimum shared-route is found by the ant colony optimization (ACO) algorithm. The proposed hybrid model integrates metric and topological GIS-based methods with a metaheuristic algorithm. It is implemented via a bot “@Hamsafar” within the platform of a robot Telegram messenger. The proposed ridesharing application is applied with 220 passengers and 70 drivers with 61 shared trips in District # 6 of Tehran, Iran. The system are evaluated based on the statistical results, usability questionnaire, time performance, and comparison to some other metaheuristic approaches which in turn demonstrate the efficiency of the proposed algorithm.
... Measuring how much the mobility of an individual is compatible with alternative transport modalities -in our case, EVs and car sharing/pooling, both with their own constraints -is a not well defined problem. Existing work measured the ratio of trips that are perfectly compatible with them 1 3 (Guidotti et al. 2017;Janssens et al. 2012), or simply compare general mobility demand (based on trip length distribution and other overall descriptors, for instance, as in Donati et al. 2015) but without a more realistic evaluation of the effort required on behalf of the user to adapt her whole mobility. In the case of EVs, that means changing times and routes to intercept charging stations when needed. ...
Car telematics is a large and growing business sector aiming to collect mobility-related data (mainly private and commercial vehicles) and to develop services of various nature both for individual citizens and other companies. Such services and applications include information systems to support car insurances, info-mobility services, ad hoc studies for planning purposes, etc. In this work we report and discuss some of the key challenges that a car telematics pilot application is facing within the EU project “Track and Know”. The paper introduces the overall context, the main business goals identified as potentially beneficial of big data solutions and the type of data sources that such applications can rely on (in particular, those available within the project for experimental studies), then discusses initial results of the solutions developed so far and ongoing lines of research. In particular, the discussion will focus on the most relevant applications identified for the project purposes, namely new services for car insurance, electric vehicles mobility and car- and ride-sharing.
... The authors considered the influence of incremental weight carried by each vehicle (in picking-up or dropping-off extra passengers during the trip) on carpooling's fuel consumptions. Guidotti et al. [33] forecasted the potential economic and environmental impact of carpooling in Pisa and Florence, considering the number of private cars sold and the estimated carpooling usage rate. These three aforementioned studies focused on potential carpooling participants and conducted macroscopic analysis using traditional survey data, which only conducted a rough estimate for carpooling fuel using and were not true representative of the actual carpooling market. ...
... The authors considered the influence of incremental weight carried by each vehicle (in picking-up or dropping-off extra passengers during the trip) on carpooling's fuel consumptions. Guidotti et al. [33] forecasted the potential economic and environmental impact of carpooling in Pisa and Florence, considering the number of private cars sold and the estimated carpooling usage rate. These three aforementioned studies focused on potential carpooling participants and conducted macroscopic analysis using traditional survey data, which only conducted a rough estimate for carpooling fuel using and were not true representative of the actual carpooling market. ...
As an eco-friendly and convenient transportation mode, mobile internet-based carpooling has achieved mushroom growth in many cities in recent years. Theoretical studies have verified that ridesharing is not only beneficial to drivers and passengers but particularly to the environment. Nevertheless, the exact impact of ridesharing on energy consumption and exhaust emission has been barely explored based on real carpooling data. In this study, using massive mobile internet based carpooling data offered by DiDi Company, a trip-specific model was initially proposed to study the intrinsic mechanism of carpooling services and then estimate the fuel savings of individual carpooling trip. According to the estimation results, delicacy subsidy strategies under the Personal Carbon Trading scheme were suggested to guarantee the moderation and equity in promoting carpooling services. The developed methodology was further tested in the case city of Beijing and associated results showed that ridesharing could be a feeder for public transit to support the commuting demands of workers living in suburban. More importantly, the fuel savings of ridesharing are considerable, every trip saving 1.23 L on average, and the carbon subsidies are moderate, per trip reaching ¥5.38 with the strictest subsidy ceiling. From the spatial-temporal perspective, the Chaoyang district and the daily peak-hour period generate the largest number of both ridesharing orders and fuel savings. All the results demonstrate that the trip-specific model has the advantages of delicacy, reliability and accuracy, which could facilitate the estimation on the trip-specific fuel savings and the formulation of carpooling promotion strategies.
... This system provides an opportunity to make more effective usage of the private vehicles while maintaining the privileges of individual mobility. By reducing the number of vehicles used for private trips, carpooling can increase vehicle occupancy rates, and could substantially increase the efficiency of urban transportation systems, which may potentially help surmount significant societal concerns of the large cities, such as environmental pollution, traffic congestion, parking problem, and severe fuel consumption (Calvo, Wolfler, de Luigi, Haastrup, & Maniezzo, 2004;Correia, Jorge, & Antunes, 2014;Dakroub, Carl Michael, Fayez, Mariette, & Hassan, 2013;Guidotti, Nanni, Rinzivillo, Pedreschi, & Giannotti, 2017). Although the idea of carpooling is not new, it has not often been applied systematically. ...
Carpooling is an environmentally friendly transportation system. It can efficaciously help resolve a variety of societal concerns of the urban areas, ranging from traffic congestion to environmental pollution. In this paper, we propose a new mathematical model to solve the carpooling problem. The model simultaneously minimizes the costs of travel times, the vehicle use, and the vehicle delays. An exact solution method based on Branch-and-Bound (B&B) algorithm is proposed to efficiently obtain the optimal solution of the problem. In order to find the near-optimal solutions for large-scale problems, a heuristic beam search algorithm is introduced, which is based on the partial relaxation of some fathoming criteria applied in our proposed B&B. The computational experiments are conducted, based upon the transportation network of Isfahan city, Iran. The results demonstrate the great capability of the proposed exact solution method in terms of both computational solving time required and the number of the evaluated nodes, in comparison with CPLEX software package. The findings of this research can be applied to solve the carpooling problem compatible to the real-life situations. ARTICLE HISTORY
... This work proposes another perspective of carpooling system that utilize semantic features of the trip and offer the passenger another destination based on the trip type (whether routine or occasional trip). In order to extract trip profile, the proposed model utilized mobility profiles of individual traveler used in [11]. The main objective of the proposed car pooling model is to minimize the congestion in the streets by reducing car flow and combine different passengers in the same taxi to the same destination which leads to minimize the fare cost to the passenger. ...
... The literature offers a number of case studies [21][22][23] focusing on the impacts [24], travel behaviour [20,[25][26][27], comparisons of car-sharing systems [28], technical papers on how to coordinate/ manage shared mobility [29][30][31][32][33], and hypothetical uptake [34][35][36][37][38]. The research has also focused on Uber [39][40][41], probably because it has grown substantially since it started in 2009. ...
Shared mobility or mobility in the sharing economy is characterised by the sharing of a vehicle instead of ownership, and the use of technology to connect users and providers. Based on a literature review, the following four emerging models are identified: (1) peer to peer provision with a company as a broker, providing a platform where individuals can rent their cars when not in use; (2) short term rental of vehicles managed and owned by a provider; (3) companies that own no cars themselves but sign up ordinary car owners as drivers; and (4) on demand private cars, vans, or buses, and other vehicles, such as big taxis, shared by passengers going in the same direction. The first three models can yield profits to private parties, but they do not seem to have the potential to reduce congestion or CO2 emissions substantially. The fourth model, which entails individuals not only sharing a vehicle, but actually travelling together at the same time, is promising in terms of congestion and CO2 emissions reductions. It is also the least attractive to individuals, given the disbenefits in terms of waiting time, travel time, comfort, and convenience, in comparison with the private car. Potential incentives to encourage shared mobility are also discussed, and research needs are outlined.
... Although previous studies have measured the outcomes of carpooling (Guidotti et al., 2017) or ride-sharing (Cici et al., 2014;Dong et al., 2018;Hong et al., 2017), the potential benefits addressed by researchers are merely trip reductions. The overall consideration of traffic condition improvement resulting from carpooling remains lacking. ...
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.
... The scale of car-pooling is difficult to assess as many trips are informally made. Some researchers position car-pooling as the second most popular way of commuting [9], although there are many differences between countries and continents. ...
The wide accessibility of European citizens to cars results in problems caused by their excessive use as a means of urban transport. Given this situation, it is necessary to find new solutions for the more efficient use of passenger cars in cities. This problem affects almost all European cities, including those in Poland. The paper analyses the level of motorization and modal split in Polish cities with county status, while selected European cities serve as a background to determine the scale of the problem. In the search of solutions in relation to Poland, an analysis of different documents outlining the directions of urban mobility was conducted. One of these documents concerned the promotion of car-pooling, the history of which dates back to the Second World War and the 1950s. Initially introduced in the USA, its increasing development in European cities has been witnessed in recent years. Research on the evaluation of real car-pooling in Polish cities was conducted in Gdynia by the authors of this study. The results of marketing research presented in the article have determined the degree to which participants in urban mobility are inclined to take part in car-pooling schemes in Polish cities.
Keywords: car-pooling; motorization; marketing research
... Individual Mobility profiles enable several applications, ranging from deeper traffic analyses (indeed, the traffic traversing a given area can now be described also by a systematicity index, measuring the percentage of trips that are routinaries for the individuals involved) and the creation of predictive models (as in the case of MyWay [78], where ongoing trips are compared against the user's routines, and in case of match they are used to predict how the trip will continue) to services like carpooling [48]. ...
During the last 35 years, data management principles such as physical and logical
independence, declarative querying and cost-based optimization have led to profound
pervasiveness of relational databases in any kind of organization. More importantly, these
technical advances have enabled the first round of business intelligence applications and
laid the foundation for managing and analyzing Big Data today.
... greedy individual) are tweaked in order to achieve significant collective benefits. A simple (but more socially challenging) way to overcome the infrastructure problem is recommendations for car pooling as suggested by Guidotti, Nanni, Rinzivillo, Pedreschi, and Giannotti (2016) . ...
Autonomous vehicles are soon to become ubiquitous in large urban areas, encompassing cities, suburbs and vast highway networks. In turn, this will bring new challenges to the existing traffic management expert systems. Concurrently, urban development is causing growth, thus changing the network structures. As such, a new generation of adaptive algorithms are needed, ones that learn in real-time, capture the multivariate nonlinear spatio-temporal dependencies and are easily adaptable to new data (e.g. weather or crowdsourced data) and changes in network structure, without having to retrain and/or redeploy the entire system.
... The authors in [17] develops a model for the carpooling problem that incorporates pre-matching information (e.g., previous accepted passengers). Network analytics is used in [18] travellers in a given territory with a higher change to create a carpooling community, and the predisposition of users to be either drivers or passengers in a shared car. A measure of enjoyability for a carpooling ride is defined in [7] based on social similarities between any two users and tendency of a person to group with similar ones. ...
Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules. A number of car pooling matching services have been designed in order to efficiently find successful ride matches in a given pool of drivers and potential passengers. However, it is now recognised that many non-monetary aspects and social considerations, besides simple mobility needs, may influence the individual willingness of sharing a ride, which are difficult to predict. To address this problem, in this study we propose GOTOGETHER, a recommender system for car pooling services that leverages on learning-to-rank techniques to automatically derive the personalised ranking model of each user from the history of her choices (i.e., the type of accepted or rejected shared rides). Then, GOTOGETHER builds the list of recommended rides in order to maximise the success rate of the offered matches. To test the performance of our scheme we use real data from Twitter and Foursquare sources in order to generate a dataset of plausible mobility patterns and ride requests in a metropolitan area. The results show that the proposed solution quickly obtain an accurate prediction of the personalised user's choice model both in static and dynamic conditions.
A shift from privately owned vehicles to shared mobility services can affect mobility, energy consumption, and vehicle emissions. Existing literature on ridesharing services has focused on evaluating its traffic and economic impacts. In this study, we propose an integrated framework to analyze the efficiency and environmental benefits of ridesharing on a regional scale. The framework utilizes an agent-based traffic simulation package (i.e., SUMO) to replicate traffic activities for commuting trips in a mid-size city, Chattanooga, Tennessee, based on real-world travel-demand data. We construct scenarios representing different ridesharing strategies and penetrations. The simulation and results analysis show that with a ridesharing ratio of 5%–75% over travel demand in a city scale, many (65%–75%) ridesharing travelers will experience up to a 15-minute delay. About 80% of drive-alone travelers will arrive earlier compared with no ridesharing scenario. The average early arrival time would be 5.6 min for all drive-alone travelers. The results also show ridesharing services can achieve a 2%–50% reduction in total city-scale vehicle emissions and energy consumption compared with the no ridesharing scenario. The framework and results of this study can be helpful to transportation practitioners to evaluate environmental benefits when implementing ridesharing services on a city scale.
Researching the field of business intelligence and analytics (BI & A) has a long tradition within information systems research. Thereby, in each decade the rapid development of technologies opened new room for investigation. Since the early 1950s, the collection and analysis of structured data were the focus of interest, followed by unstructured data since the early 1990s. The third wave of BI & A comprises unstructured and sensor data of mobile devices. The article at hand aims at drawing a comprehensive overview of the status quo in relevant BI & A research of the current decade, focusing on the third wave of BI & A. By this means, the paper’s contribution is fourfold. First, a systematically developed taxonomy for BI & A 3.0 research, containing seven dimensions and 40 characteristics, is presented. Second, the results of a structured literature review containing 75 full research papers are analyzed by applying the developed taxonomy. The analysis provides an overview on the status quo of BI & A 3.0. Third, the results foster discussions on the predicted and observed developments in BI & A research of the past decade. Fourth, research gaps of the third wave of BI & A research are disclosed and concluded in a research agenda.
Carpooling promises benefits to the environment and consumers alike, but research has shown that there are multiple practical challenges to realizing these benefits. One possible solution is the addition of meeting points (hubs) in the design of carpooling systems. Recent studies indicate that introducing hubs increases both carpool participation and system-wide savings. Little to no research has been conducted, however, to understand the origin of these savings, determine their utility in different carpooling models, or detailing how hubs should be introduced (i.e. as a mandate or option). Additionally, we know nothing about the impact of hubs on self-organizing carpools (the primary means of creating carpooling systems). To address these gaps, we studied the impact of meeting points on two types of carpooling models (pick-up/drop-off and to/from), using two solution paradigms (centralized and self-organized), with hubs that were either mandated or optional. Our findings show that adding the option of meeting hubs improves system-wide savings from carpooling. These findings, along with our introduction of a new efficient carpool enumeration technique, have important practical implications for the design of modern carpooling systems in the development of more effective and sustainable uses of transportation resources.
The explosion of online shopping brings great challenges to traditional logistics industry, where the massive parcels and tight delivery deadline impose a large cost on the delivery process, in particular the last mile parcel delivery. On the other hand, modern cities never lack transportation resources such as the private car trips. Motivated by these observations, we propose a novel and effective last mile parcel delivery mechanism through car trip sharing, to leverage the available private car trips to incidentally deliver parcels during their original trips. To achieve this, the major challenges lie in how to accurately estimate the parcel delivery trip cost and assign proper tasks to suitable car trips to maximize the overall performance. To this end, we develop Car4Pac, an intelligent last mile parcel delivery system to address these challenges. Leveraging the real-world massive car trip trajectories, we first build up a 3D (time-dependent, driver-dependent and vehicle-dependent) landmark graph that accurately predicts the travel time and fuel consumption of each road segment. Our prediction method considers not only traffic conditions of different times, but also driving skills of different people and fuel efficiencies of different vehicles. We then develop a two-stage solution towards the parcel delivery task assignment, which is optimal for one-to-one assignment and yields high-quality results for many-to-one assignment. Our extensive real-world trace driven evaluations further demonstrate the superiority of our Car4Pac solution.
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.
Distances between locations traveled by a carpool driver in a carpooling system may be initially estimated by calculating direct, straight line distances between each of the location points. Travel speeds may also be initially estimated using an expected maximum vehicle speed, which may be a maximum speed limit. An estimated travel time may then be calculated from this data to initially designate passengers as eligible or ineligible for carpooling with a carpool driver.
Carpooling is thought to be part of the solution to resolve traffic congestion in regions where large companies dominate the traffic situation because coordination and matching between commuters is more likely to be feasible in cases where most people work for a single employer. Moreover, carpooling is not very popular for commuting. In order for carpooling to be successful, an online service for matching commuter profiles is indispensable due to the large community involved. Such service is necessary but not sufficient because carpooling requires rerouting and activity rescheduling along with candidate matching. We advise to introduce services of this kind using a two step process: (1) an agentbased simulation is used to investigate opportunities and inhibitors and (2) online matching is made available. This paper describes the challenges to build the model and in particular investigates possibilities to derive the data required for commuter behavior modeling from big data (such as GSM, GPS and/or Bluetooth).
Both public policy-makers and private companies promote carpooling as a commuting alternative in order to reduce the number of Single Occupant Vehicle (SOV) users. The Belgian questionnaire Home-To-Work-Travel (HTWT) is used to examine the factors which explain the share of carpooling employees at a worksite. The modal split between carpooling and rail use was also subject of the analysis. The number of observations in the HTWT database (n=7460) makes it possible to use more advanced statistical models: such as multilevel regression models which incorporate, next to the worksite level, also the company and economic sector levels. As a consequence, a more employer-oriented approach replaces the traditional focus of commuting research on the individual. Significant differences in modal split between economic sectors appeared. The most carpool-oriented sectors are construction and manufacturing, while rail transport is more popular in the financial and public sector. Carpooling also tend to be an alternative at locations where rail is no real alternative. Next to this, regular work schedules and smaller sites are positively correlated with a higher share of carpooling employees. Finally, no real evidence could be found for the effectiveness of mobility management measures which promote carpooling. However, most of these measures are classified in the literature as less effective and a case study approach should complete the research on mobility management initiatives.
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. The state-of-the-art research work on agent-based modeling is limited to a number of technical and empirical studies that are unable to handle the complex agent behavior in terms of coordination, communication and negotiations. 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 plan to, as a part of the future work, develop a prototype of our agent- based carpooling application based on the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data.
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.
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 interested agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans and (v) provide a feedback to all concerned agents. The state-of-the-art research work on agent-based modeling is limited to a number of technical and empirical studies that are unable to handle the complex agent behavior in terms of coordination, communication and negotiations. In this paper we present a conceptual design of an agent-based model (ABM) for the carpooling application that serves as a proof of concept. Our agent-based 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 agent-based 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 plan to, as a part of the future work, develop a prototype of our agent-based carpooling application on the basis of the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data.
An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile
and a set of periodically recurring trips. The Global CarPooling Matching Service (GCPMS) shall advise registered
candidates on how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in
a graph; the edges are labeled with the probability for negotiation success while trying to merge planned trips by
carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person
and trip characteristics and on the other hand the negotiation feedback. The GCPMS provides advice by maximizing
the expected value for negotiation success. This paper describes possible ways to determine the optimal advice and
estimates computational scalability using real data for Flanders.
Understanding how drivers respond to fuel price changes has important implications for highway congestion, accidents, carbon policy, local air pollution and taxation. We examine the under-explored relationship between fuel prices and carpooling. Using a simple theoretical model we show that traffic flows in mainline lanes decrease when fuel prices increase. However in carpool (HOV) lanes, flow can either increase or decrease. Traffic flows in mainline lanes are shown to be more responsive to price changes when the presence of a carpool lane provides a substitute to driving alone. We test these predictions using eight years of traffic data for 1,700 locations in Los Angeles. The mean elasticity of flow with respect to fuel price is 0.136 for HOV lanes. This implies 10 additional carpools per hour for a 10\% increase in fuel price. For mainline lanes, flow elasticities are -0.083 and -0.050 for highways with and without an HOV lane. These estimates imply that the mean highway with an HOV lane experiences a 30 percent larger decrease in hourly flow compared to the mean highway without an HOV lane. Flows in HOV lanes show an immediate decrease following a price increase but respond positively to price increases over time, which suggests time is an important input to carpool formation.
In the current field experiment we evaluate a structural solution to a real-life social dilemma by examining the effects of a carpool priority lane on judgments and preferences concerning the decision to commute by carpool (i.e., the presumed cooperative option) or driving alone (i.e., the presumed noncooperative option). Our general hypothesis was that this intervention would evoke a process of self-justification in solo drivers, arising from feelings of relative deprivation andlor cognitive dissonance. Consistent with predictions, we found that in comparison with judgments made before the implementation of the carpoollane, solo drivers tended to decrease the importance of an attribute inherently linked to carpooling (i.e., low travel costs) and to increase the importance of an attribute inherently linked to driving alone (i.e., flexibility). Moreover, solo drivers exhibited a weaker preference for ca/pooling af ter the establishment of the ca/pool lane. This finding suggests that the negative side effects of this structural measure were more pronounced than the intended ca/pool-promoting effects .
The WiSafeCar (Wireless Traffic Safety Network between Cars) project aims at increasing the performance and reliability of the wireless transport and to provide traffic safety improvements. Within the context of this project, we have designed a Dynamic Carpooling System that will optimize the transport utilization by the ride sharing among people who usually cover the same route. An initial prototype of the system has been developed by using NetLogo. The information obtained from this simulator will be used to study the functioning of the clearing services, the current business models and to propose new ones. The first results seem encouraging, and the users have many economical advantages thanks to the sharing of costs which allows the individuals to retrench expenses and to contribute to the use of green technologies.
Community discovery in complex networks is an interesting problem with a
number of applications, especially in the knowledge extraction task in social
and information networks. However, many large networks often lack a particular
community organization at a global level. In these cases, traditional graph
partitioning algorithms fail to let the latent knowledge embedded in modular
structure emerge, because they impose a top-down global view of a network. We
propose here a simple local-first approach to community discovery, able to
unveil the modular organization of real complex networks. This is achieved by
democratically letting each node vote for the communities it sees surrounding
it in its limited view of the global system, i.e. its ego neighborhood, using a
label propagation algorithm; finally, the local communities are merged into a
global collection. We tested this intuition against the state-of-the-art
overlapping and non-overlapping community discovery methods, and found that our
new method clearly outperforms the others in the quality of the obtained
communities, evaluated by using the extracted communities to predict the
metadata about the nodes of several real world networks. We also show how our
method is deterministic, fully incremental, and has a limited time complexity,
so that it can be used on web-scale real networks.
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-data sets there does not even exist a global parameter setting for which the result of the clustering algorithm describes the intrinsic clustering structure accurately. We introduce a new algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information which is equivalent to the density-based clusterings corresponding to a broad range of parameter settings. It is a versatile basis for both automatic and interactive cluster analysis. We show how to automatically and efficiently extract not only 'traditional' clustering information (e.g. representative points, arbitrary shaped clusters), but also the intrinsic clustering structure. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, we introduce an appropriate visualization technique. Both are suitable for interactive exploration of the intrinsic clustering structure offering additional insights into the distribution and correlation of the data.
Carpooling reduces the number of cars on the road, reduces gas consumption, and saves participants money. In order to free carpooling from rigid schedules and preplanning, just-in-time carpooling allows a large member base of passengers and drivers to be matched with each other automatically and instantly, allowing for on-the-spot arrangement of rides. A mobile phone call or text message initiates an automatic process in which drivers and passengers are matched to a shared ride wherever and whenever they need it, without the scheduling constraints of traditional carpooling. This program faces a number of challenging barriers in technology and behavioral science. These include the creation of a seamless interaction between mobile phones and the internet server, voice recognition and SMS solutions, safety of mobile phone use and driving, and motivation, safety, and trust among participating members of the carpooling community.
We study several classes of related scheduling problems including the carpool problem, its generalization to arbitrary inputs and the chairman assignment problem. We derive both lower and upper bounds for online algorithms solving these problems. We show that the greedy algorithm is optimal among online algorithms for the chairman assignment problem and the generalized carpool problem. We also consider geometric versions of these problems and show how the bounds adapt to these cases.
To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation, which reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network--the optimal number of levels and modular partition at each level--with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines: life sciences, physical sciences, ecology and earth sciences, and social sciences. In general, we find shallow hierarchical structures in globally interconnected systems, such as neural networks, and rich multilevel organizations in systems with highly separated regions, such as road networks.
Some of the transportation energy consumed during peak commuter periods is wasted through slow running in congested traffic. Strategies to increase average vehicle occupancy (and reduce vehicle counts and congestion) could be expected to be at the forefront of energy conservation policies. Casual carpooling (also called “slugging”) is a system of carpooling without trip-by-trip pre-arrangement. It operates in three US cities, and has been suggested in New Zealand as a strategy for managing transportation challenges when oil prices rise. The objective of the paper is to find out if casual carpooling reduces energy consumption, and if so, how much. Energy consumption by single occupant vehicles; casual carpool vehicles; and a mix of buses and single occupant vehicles; are estimated and compared, and the impact on the rest of the traffic is calculated. The paper estimates that casual carpooling in San Francisco is conserving in the order of 1.7 to 3.5 million liters of gasoline per year, or 200-400 liters for each participant, much of which comes from the impact on the rest of the traffic. The paper concludes by calling for applied research to discover how to catalyze casual carpooling in other cities as a means of reducing transportation energy consumption.
Home to work travel remains the prime focus of mobility management policies, in which the promotion of carpooling is one of the main strategies. Besides governments, employers are key players in this strive for a more sustainable commute. However, commuting research tends to focus on individual commuters and their place of residence, rather than on workplaces and company-induced measures. Therefore, this paper takes the workplace as research unit to analyse the popularity of carpooling in Belgium. After an exploratory (spatial) data analysis, we incorporate three groups of factors in a multilevel regression model which predicts the share of carpooling at large workplaces: location (accessibility), organisation (activity sector), and promotion (carpool-oriented mobility management measures). Higher levels of carpooling are found at less accessible locations, and in the activity sectors construction, manufacturing and transport. This analysis gives insight in the determinants of carpooling, and may thus contribute to the development of sustainable transport policies.
Carpooling for commuting can save cost and helps in reducing pollution. An automatic Web based Global CarPooling Matching Service (GCPMS) for matching commuting trips has been designed. The service supports carpooling candidates by supplying advice during their exploration for potential partners. Such services collect data about the candidates, and base their advice for each pair of trips to be combined, on an estimate of the probability for successful negotiation between the candidates to carpool. The probability values are calculated by a learning mechanism using, on one hand, the registered person and trip characteristics, and on the other hand, the negotiation feedback. The problem of maximizing the expected value of carpooling negotiation success was formulated and was proved to be NP-hard. In addition, the network characteristics for a realistic case have been analyzed. The carpooling network was established using results predicted by
the operational FEATHERS activity based model for Flanders (Belgium).
Smart phones and social networking tools allow to collect large-scale data about mobility habits of people. These data can support advanced forms of sharing, coordination and cooperation possibly able to reduce the overall demand for mobility. Our goal is to develop a recommender system - to be integrated in smart phones, tablets, and in-vehicle platforms - capable of identifying opportunities for sharing cars and rides. We present a methodology, based on the extraction of suitable information from mobility traces, to identify rides along the same trajectories that are amenable for ride sharing. We provide experimental results showing the impact of this technology and we illustrate a Web-based platform implementing the key concepts presented.
Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver co-riders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this paper, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized device, called TaxiBox. In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers.
To improve coRide's efficiency in mileage reduction, we formulate a NP-hard route calculation problem under different practical constraints. We then provide (i) an optimal algorithm using Linear Programming, (ii) a 2 approximation algorithm with a polynomial complexity, and (iii) its corresponding online version. To encourage coRide's adoption, we present a win-win fare model as the incentive mechanism for passengers and drivers to participate. We evaluate coRide with a real world dataset of more than 14,000 taxicabs, and the results show that compared with the ground truth, our service can reduce 33% of total mileage; with our win-win fare model, we can lower passenger fares by 49% and simultaneously increase driver profit by 76%.
While representing an important element in the development of a sustainable transport system, car sharing, at least in its traditional form, is not useful for standard commuter trips during which vehicles remain largely idle throughout the day. In fact, such idling has been considered a crucial limitation in extending the potential of car sharing to date. We report the results of a national two-year field study on a commuter-adjusted version of car sharing. Here, a rail company offers (electric) cars to commuters in order to allow them access from their home to the nearest train station. At the same time, the rail company organizes the day use of the car by businesses such as postal services or mobile health care. We find the following evaluating costs, market potential, and environmental merits. The two previously separate users now share one car, with thus more efficient use of the capital stock “car” which allows covering of the additional overhead costs. The potential market is likely to be of sufficient interest for a national rail company operating in a country with a settlement structure such as that which exists in Austria. The environmental effect depends on the share of electric vehicles and the generation mix of electricity.
Carpooling is the commuting mode of 18 to 20% of American workers, but relatively little definitive information has been available on who carpools, how and why. Based on data from the 1977–1978 Nationwide Personal Transportation Survey, this paper analyzes the characteristics of carpoolers, distinguishes among different types of carpoolers, identifies the key differences between carpoolers and drive alone and transit commuters, describes how commuters carpool, and offers explanations of why commuters carpool. The paper also addresses the issue of the feasibility of a substantial increase in carpool mode share.
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.
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.
In the last few years many real-world networks have been found to show a
so-called community structure organization. Much effort has been devoted in the
literature to develop methods and algorithms that can efficiently highlight
this hidden structure of the network, traditionally by partitioning the graph.
Since network representation can be very complex and can contain different
variants in the traditional graph model, each algorithm in the literature
focuses on some of these properties and establishes, explicitly or implicitly,
its own definition of community. According to this definition it then extracts
the communities that are able to reflect only some of the features of real
communities. The aim of this survey is to provide a manual for the community
discovery problem. Given a meta definition of what a community in a social
network is, our aim is to organize the main categories of community discovery
based on their own definition of community. Given a desired definition of
community and the features of a problem (size of network, direction of edges,
multidimensionality, and so on) this review paper is designed to provide a set
of approaches that researchers could focus on.
Thanks to the important and increasing growth of the carpooling phenomenon throughout the world, many researchers have particularly focused their efforts on this concept. Researches led to many systems affording carpooling service not usually effective. In fact, most of them present multiple drawbacks regarding automation, functionalities, accessibility, etc. Besides, only few researchers focused on real time carpooling concept without producing promising results. To address these gaps, we introduce a novel approach called DARTiC: a Distributed dijkstra for the implementation of a Real Time Carpooling system based on the multi-agent concept, we particularly focus on the distributed and dynamic aspect within dijkstra's implementation. A new modeling of the served network highlights the distributed architecture, helping to perform decentralized parallel process. This helped to take into consideration different aspects we should be involved in, especially optimization issue. Users' requests must be performed in a reasonable time and responses should be as efficient as possible with regards to the fixed optimization criteria.
In this paper, we study approximation algorithms for several NP-hard facility location problems. We prove that a simple local search heuristic yields polynomial-time constant-factor approximation bounds for metric versions of the uncapacitated k-median problem and the uncapacitated facility location problem. (For the k-median problem, our algorithms require a constant-factor blowup in the parameter k.) This local search heuristic was first proposed several decades ago and has been shown to exhibit good practical performance in empirical studies. We also extend the above results to obtain constant-factor approximation bounds for the metric versions of capacitated k-median and facility location problems.
The effectiveness of California’s 1171 mile High Occupancy Vehicle (HOV) system is assessed using peak hour traffic data from 700+ loop detector stations over many months. The study reaches the following conclusions.(1) HOV lanes are under-utilized: 81% of HOV detectors measure flows below 1400 vehicles per hour per lane (vphpl) during the PM peak hour. (2) Many HOV lanes experience degraded operations: 18% of all HOV miles during the AM peak hour and 32% during the PM peak hour have speeds below 45 mph for more than 10% of weekdays. (3) HOV lanes suffer a 20% capacity penalty, achieving a maximum flow of 1600 vphpl at 45 mph vs. maximum flow above 2000 vphpl at 60 mph in general purpose (GP) lanes. (4) HOV lanes offer small travel time savings. The mean savings over a random 10-mile route on an HOV lane vs. the adjacent GP lane is 1.7 min and the median is 0.7 min; however, HOV travel times are more reliable. (5) Travel time savings do not provide a statistically significant carpooling incentive. (6) A system with one HOV lane and three GP lanes carries the same number of persons per hour as a system with four GP lanes. (7) HOV lanes reduce overall congestion slightly only when the general purpose lanes are allowed to become congested.Despite these findings, HOV facilities can play a useful role in a well-managed freeway system in California. In particular, they can be useful if there is a significant number of buses or vanpools; as a 2-lane HOV/HOT facility, which eliminates capacity loss; and, with efficient metering, as a HOV/HOT bypass at the on-ramps.
In this paper we introduce a methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles. We instantiate the profile matching problem to a specific application context, namely proactive car pooling services, and therefore develop a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain. In order to evaluate the impact and robustness of the methods introduced, two experiments are reported, which were performed on a massive dataset containing GPS traces of private cars: (i) the impact of the car pooling application based on profile matching is measured, in terms of percentage shareable traffic; (ii) the approach is adapted to coarser-grained mobility data sources that are nowadays commonly available from telecom operators. In addition the ensuing loss in precision and coverage of profile matches is measured.
Suppose k states form a union and every year a union chairman has to be selected in such a way that at any time the accumulated number of chairmen from each state is proportional to its weight. In this paper a simple algorithm for a chairman assignment is given which guarantees a small discrepancy. The situation that not only states form unions, but also unions form federations, etc., with one overall organization is also investigated.
It is often argued in the US that HOV (high occupancy vehicle) lanes are wasteful and should be converted to HOT (high occupancy vehicles and toll lanes). In this paper, we construct a simple model of commuters using a highway with multiple lanes, in which commuters are heterogeneous in their carpool organization costs. We first look at the HOV lanes and investigate under what conditions introducing HOV lanes is socially beneficial. Then we examine whether converting HOV lanes to HOT lanes improves the efficiency of road use. It is shown that the result depends on functional form and parameter values. We also discuss the effect of alternative policies: simple congestion pricing without lane division; and congestion pricing with HOV lanes. The analysis using specific functional form is presented to explicitly obtain the conditions determining the rankings of HOV, HOT, and other policies based on aggregate social cost.
High-occupancy-vehicle (HOV) lanes and toll differentiation have been used as efficient measures to address growing traffic congestion problems by providing priority treatment for buses and carpools. This paper deals with carpooling behavior and optimal congestion pricing in a multilane highway with or without HOV lanes. It is shown that in the absence of HOV lanes, a uniform toll for all vehicles (independent of their number of occupants) should be charged to achieve a first-best social optimum. However, in the presence of HOV lanes, first-best pricing for a social optimum requires differentiating the toll per vehicle across segregated lanes. In the case where toll differentiation cannot be applied, the optimal uniform toll for the second-best solution in the presence of HOV lanes should be set to be a weighted average of the marginal external congestion costs between non-carpooling and carpooling commuters. Our theoretical observations have strong practical implications for combined implementation of HOV lanes and congestion pricing.
The success of a high occupancy vehicle lane in motivating people to shift to carpools and buses depends on maintaining a travel time differential between it and the adjacent general purpose lanes. This differential, in turn, depends on the level of continuing delay on the general purpose lanes. Therefore, it is clear that a high occupancy vehicle lane that will motivate people to shift to high occupancy vehicles will not eliminate congestion. Consequently, it is not clear that constructing a high occupancy vehicle lane will necessarily reduce delay more than construction of a general purpose lane. The objective of this research is to determine the circumstances in which this would be the case. The hypothesis is that such circumstances would be quite limited, and this proves to be the case. The intended benefits of high occupancy lanes are defined as reduced person-delay and reduced emissions. A model is developed to calculate these benefits for four alternatives: add a high occupancy vehicle lane, add a general purpose lane, convert an existing lane to a high occupancy vehicle lane, and do nothing. The model takes into account the initial conditions, the dynamic nature of the travel time differential between the high occupancy vehicle lane and other lanes, and the uncertainty regarding the extent to which people will shift modes. It combines queueing theory and mode choice theory and provides a robust method for comparing alternatives using a small amount of easily observed data. Application of the model in typical situations shows that with initial delays on the order of 15 min or more, adding a high occupancy vehicle lane would provide substantial reductions in delay and some reduction in emissions. However, in a wide range of such situations, adding a general purpose lane would be even more effective. Only if the initial delay is long and the initial proportion of high occupancy vehicles falls in a rather narrow range, would an added high occupancy vehicle lane be more effective. The proportion of high occupancy vehicles must be such that it allows good utilization of the high occupancy vehicle lane while maintaining a sufficient travel time differential to motivate a shift to buses or carpools. Adding a high occupancy vehicle lane to a three lane freeway will be more effective than adding a general purpose lane only if the initial maximum delay is on the order of 35 min or more and the proportion of high occupancy vehicles is on the order of 20%. Federal policies encourage construction of high occupancy vehicle lanes and restrict funding for general purpose lanes in areas that have not attained air quality standards. The findings of this research suggest a need to reconsider these policies.
This article posits that individuals can more easily form social connections with people if they are of the same race. If true, the greater the incidence among his neighbours of persons of his race, the more likely an individual is to make neighbourhood social capital connections and the more likely he is to engage in activities which require it. The article tests this idea using an indicator of individual social capital never previously studied: whether the person uses a carpool to get to work. The analysis accounts for fixed differences across neighbourhoods, and a variety of extensions address possible differential racial sorting into neighbourhoods. The evidence strongly supports the article's hypothesis. Copyright 2006 Royal Economic Society.
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