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Nowadays, ride-hailing services have been established as a part of the urban transportation. Their arrival has remade the profit structure and resulted in the unbalance of interest in taxi market. Here, we establish the service models of taxis, carpooling, and car-hailing under “Internet +” from the perspective of profit margins, to perform a comparative analysis among the different services. Results: First, Profit margins are generally higher for short trips than for long trips, though empty cruise fee to a certain degree make up for the driver’s decreased profit margins. Second, the profit margin for carpooling is roughly 1.85 times that of ride-hailing, and 1.75 times that of taxis. This shows that the sharing economy has a certain advantage. Third, Profit margins are higher and fluctuations are lower on non-work days than on work days. At last, Profit margins are roughly 1.3 times higher on non-congested roads than on congested roads. The reduced profitability on congested roads makes it even harder to catch a ride during rush hours and on congested roads. We suggest that the relevant departments make appropriate efforts to make it more attractive for drivers to take on passengers during rush hours and on congested roads, and promote the sharing in the taxi market. [ABSTRACT FROM AUTHOR]

Carpooling is an effective means of reducing traffic. A carpool team shares a vehicle for their commute, which reduces the number of vehicles on the road during rush hour periods. Carpooling is officially sanctioned by most governments, and is supported by the construction of high-occupancy vehicle lanes. A number of carpooling services have been designed in order to match commuters into carpool teams, but it known that the determination of optimal carpool teams is a combinatorially complex problem, and therefore technological solutions are difficult to achieve. In this paper, a model for carpool matching services is proposed, and both optimal and heuristic approaches are tested to find solutions for that model. The results show that different solution approaches are preferred over different ranges of problem instances. Most importantly, it is demonstrated that a new formulation and associated solution procedures can permit the determination of optimal carpool teams and routes. An instantiation of the model is presented (using the street network of Guangzhou city, China) to demonstrate how carpool teams can be determined. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Statistics on past carpooling trends in the United States are analyzed to determine its future implications for the transportation sector. There is an average of 20% decline in carpooling from 1980 to 1990. Consequently, all the demographic and economic factors should be considered against a backdrop of increasingly dispersed populations and jobs. To solve the decline, organizers of carpooling programs should engage in continuous monitoring of carpooling trends by surveying households that engage in the activity. Researchers should also recognize the obstacles to the successful surveying of the practitioners of carpooling. Finally, carpool promotion programs need a statistical device that honestly conveys what is happening.

No abstract is available for this article. [ABSTRACT FROM AUTHOR]