An Analysis of issues against the adoption of Dynamic Carpooling

Using a private car is a transportation system very common in industrialized countries. However, it causes different problems such as overuse of oil, traffic jams causing earth pollution, health problems and an inefficient use of personal time. One possible solution to these problems is carpooling, i.e. sharing a trip on a private car of a driver with one or more passengers. Carpooling would reduce the number of cars on streets hence providing worldwide environmental, economical and social benefits. The matching of drivers and passengers can be facilitated by information and communication technologies. Typically, a driver inserts on a web-site the availability of empty seats on his/her car for a planned trip and potential passengers can search for trips and contact the drivers. This process is slow and can be appropriate for long trips planned days in advance. We call this static carpooling and we note it is not used frequently by people even if there are already many web-sites offering this service and in fact the only real open challenge is widespread adoption. Dynamic carpooling, on the other hand, takes advantage of the recent and increasing adoption of Internet-connected geo-aware mobile devices for enabling impromptu trip opportunities. Passengers request trips directly on the street and can find a suitable ride in just few minutes. Currently there are no dynamic carpooling systems widely used. Every attempt to create and organize such systems failed. This paper reviews the state of the art of dynamic carpooling. It identifies the most important issues against the adoption of dynamic carpooling systems and the proposed solutions for such issues. It proposes a first input on solving the problem of mass-adopting dynamic carpooling systems.Comment: 10 pages, whitepaper, extracted from B.Sc. thesis "Dycapo: On the creation of an open-source Server and a Protocol for Dynamic Carpooling" (Daniel Graziotin, 2010

Distributed design of network codes for wireless multiple unicasts

Previous results on network coding for low-power wireless transmissions of multiple unicasts rely on opportunistic coding or centralized optimization to reduce the power consumption. This paper proposes a distributed strategy for reducing the power consumption in a network coded wireless network with multiple unicasts. We apply a simple network coding strategy called “reverse carpooling,” which uses only XOR and forwarding operations. In this paper, we use the rectangular grid as a simple network model and attempt to increase network coding opportunities without the overhead required for centralized design or coordination. The proposed technique designates “reverse carpooling lines” analogous to a collection of bus routes in a crowded city. Each individual unicast then chooses a route from its source to its destination independently but in a manner that maximizes the fraction of its path spent on reverse carpooling lines. Intermediate nodes apply reverse carpooling opportunistically along these routes. Our network optimization attempts to choose the reverse carpooling lines in a manner that maximizes the expected power savings with respect to the random choice of sources and sinks

Carpooling and employers: a multilevel modelling approach

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

Not driving alone: Commuting in the Twenty-first century

This paper investigates recent commuting trends in American workers. Unlike most studies of commuting that rely on Census data, this study utilizes the unique American Time Use Survey to detail the complex commuting patterns of modern-day workers. The data confirm what has been suspected, that incidence of driving alone has decreased substantially in recent years while carpooling has rebounded. The results from the multi-nominal logistic estimation of workers' commuting choices yield support for both the traditional economic determinants as well as for the newer, socio-economic factors. In addition to the cost savings, many commuters appear to value the social aspect of carpooling. Surprisingly, there is little evidence that the need for autonomy plays much of a factor in explaining workerÕs choice of the journey to work. The estimated short-run elasticity of carpooling with respect to real gas prices appears to be quite high and largely accounts for the significant decline in the incidence of driving alone.Ride sharing, carpooling, commuting, gasoline process, social capital

Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.0435

On proximity and hierarchy : exploring and modelling space using multilevel modelling and spatial econometrics

Spatial econometrics and also multilevel modelling techniques are increasingly part of the regional scientists‟ toolbox. Both approaches are used to model spatial autocorrelation in a wide variety of applications. However, it is not always clear on which basis researchers make a choice between spatial econometrics and spatial multilevel modelling. Therefore it is useful to compare both techniques. Spatial econometrics incorporates neighbouring areas into the model design; and thus interprets spatial proximity as defined in Tobler‟s first law of geography. On the other hand, multilevel modelling using geographical units takes a more hierarchical approach. In this case the first law of geography can be rephrased as „everything is related to everything else, but things in the same region are more related than things in different regions‟. The hierarchy (multilevel) and the proximity (spatial econometrics) approach are illustrated using Belgian mobility data and productivity data of European regions. One of the advantages of a multilevel model is that it can incorporate more than two levels (spatial scales). Another advantage is that a multilevel structure can easily reflect an administrative structure with different government levels. Spatial econometrics on the other hand works with a unique set of neighbours which has the advantage that there still is a relation between neighbouring municipalities separated by a regional boundary. The concept of distance can also more easily be incorporated in a spatial econometrics setting. Both spatial econometrics and spatial multilevel modelling proved to be valuable techniques in spatial research but more attention should go to the rationale why one of the two approaches is chosen. We conclude with some comments on models which make a combination of both techniques

Take me on a ride: The role of environmentalist identity for carpooling

Sharing does not need to involve corporate providers but can also happen on a peer-to‐ peer (P2P) basis. P2P sharing platforms who match private providers and users are thus dealing with two different customer segments. An example of this is carpooling, the sharing of a car journey. Recent years have seen considerable research on why people use sharing services. In contrast, there is little knowledge of why people may offer a good for sharing purposes. Drawing on identity theory, this paper suggests that users and providers of carpooling need to be addressed differently. A pilot study and two studies, including both actual car owners and nonowners confirm that the extent to which one identifies as an environmentalist predicts car owners' willingness to offer carpooling, but does not affect nonowners' willingness to use carpooling services. These findings remain robust when controlling for various potential confounds. Furthermore, Study 2 suggests that an environmentalist identity plays an important role for car owners' actual decision to offer a ride via an online platform. These results suggest that marketers of P2P platforms need to pursue different strategies when addressing potential users and providers on the same platform

17-09 Assessing the Impact of Air Pollution on Public Health Along Transit Routes

Transportation sources account for a large proportion of the pollutants found in most urban areas. Also, transportation activity and intensity appear likely to contribute to the risk of respiratory disease occurrence. This research investigates the impacts of transportation, urban design and socioeconomic characteristics on the risk of air pollution-related respiratory diseases in two of the biggest MSAs (Metropolitan Statistical Areas) in the US, Dallas-Fort Worth (DFW) and Los Angeles at the block group (BG) level, by considering the US Environmental Protection Agency’s respiratory hazard quotient (RHQ) as the dependent variable. The researchers identify thirty candidate indicators of disease risk from previous studies and use them as independent variables in the model. The study applies a three-step modeling including Principal Component Analysis (PCA), Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) to reach the final model. The results of this study demonstrate strong spatial correlations in the variability in both MSAs which help explain the impact of the indicators such as socioeconomic characteristics, transit access to jobs, and automobile access on the risk of respiratory diseases. The populations living in areas with higher transit access to jobs in urbanized areas and greater automobile access in more rural areas appear more prone to respiratory diseases after controlling for demographic characteristics