This paper will review the match between single driver and single rider in online taxi services through a resource sharing (sharing platform) for the operators with the objectives to maximize the profit for drivers (operators) and minimize waiting time for passengers so that the matching rate is higher. A low matching rate between rider and driver can cause the consumer to drop the services. The matching between single driver and single rider in online taxi services through a sharing platform scheme is formulated in maximum weighted bipartite matching problem. To solve the proposed model, we use Kuhn Munkres Algorithm, while to solve the shortest path for the driver to pick up the passenger and the shortest path of passenger's origin destination, modified Dijkstra with adaptive algorithm based on Wei Peng et.al (2012) is used. Based on illustrative example with several cases, we found a resource sharing scenario can optimize the matching between driver and rider and moreover can solve the surge pricing problem which is deemed as less transparant to customer
Beside the ridesoucing service, ridesplitting service is also offered by Transport Network Companies (TNC). The ridesplitting service have more benefit than ridesourcing because it is using the concept of carsharing. The current condition for ridesplitting service is not popular and only have small demand than ridesourcing service. This study aims to establish a mode choice model between ridesourcing and ridesplitting service in DKI Jakarta and to estimate the potential of demand shifting from ridesourcing to ridesplitting service in DKI Jakarta. The mode choice model is developed from binary logit model with stated preference survey using fare saving, additional time travel and security presented by gender parameter of ridesplitting service. the sensitivity of logit model show that highest sensitivity rate to increase mode switching to ridesplitting service is in 20% to 50% fare saving level. The probability of current condition to switch to ridesplitting service is 20%.
Ridesharing is transportation mode which brings together its participant whose similar itineraries and time schedule. The problem is how to compute matching requests with the large number of participants in short optimization time. For m driver n rider at the same time period, there is mxn driver-rider combination (possible match / pm) which will be considered whether time feasibility constraint is satisfied or not, if it satisfied then the pair is feasible match (fm). This paper purposed Agglomerative Hierarchical Clustering (AHC) to be applied for determining pm. By applying AHC, the pair which is not feasible based on their location is eliminated. AHC is clustering which merge two closest cluster, recursively, until all objects merge into one cluster. After we get fm set, we will check is there any pair whose distance saving less than zero? If it is, then it will be eliminated. To decide pairs that will do ridesharing trip (optimum match) we use Hungarian Algorithm. As the result, we obtain same number of optimum matched with and without applying AHC. At the process, by applying AHC, we can reduce the number of pm, so that total calculation of the driver-rider match that will be optimized is less.
A Dynamic Dial-a-Ride Problem with Money as an Incentive (DARP-M) is a problem of finding optimal route to serve requests demand which uses taxi-sharing system with cost constraint. Taxi-sharing is a system where individual customer share vehicle with other customers, who has the same or similar origin, destination, and travel time. The optimal solution is the solution that can minimize the cost of each trip request. This study discusses DARP-M to optimize the use of taxi-sharing. The search for the DARP-M solution in this research uses the insertion heuristic method for construction of initial solution and the large neighborhood search method for the optimal solution determination. Then, the experiment uses three times periods, from which the experiment result shows the large neighborhood search method can minimize customers travel cost up to 27.40 % less than the cost of private rides.
Nowadays, a ride-sharing system is a trend among society for traveling. The ride-sharing system is a solution that can be developed to reduce the congestion because of the high amounts of the vehicle on the road. Taxi as an alternative transportation in an urban area can impose the ride-sharing system. Taxi-sharing aims to maximize the utilization of taxi capacity, thereby reduces the fare for passengers, increases the income for taxi operator, and reduces congestion, gas emission, as well as fuel consumption. In order to maximize the benefits of the taxi-sharing system usage, we need to optimize taxi routes and match requests that share taxi service. In this paper, we used a mixed integer programming problem as in Hosni et al (2014) to make a model of optimization of the taxi-sharing problem, then solved the problem by using a tabu search method. The experiments showed that the tabu search method could increase the income of taxi operator up to 10 - 14 %.
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