Clustering Analysis of Ridership Patterns at Subway Stations: A Case in Nanjing, China

Cruising for parking creates a moving queue of cars that are waiting for vacated parking spaces, but no one can see how many cruisers are in the queue because they are mixed in with normal cars that are actually going somewhere. In order to mitigate the influence of cruising for parking on the normal cars, the park-and-visit cruising tests with GPS and cameras was applied to collect the behavior of the cruisers, and the videotapes of traffic flows were used to measure the volume of cruising cars and the traffic status of normal cars, simultaneously. On this basis, a parking time model based on proportional hazard-based duration model was proposed, and the factors affecting cruise for parking were analyzed, including the volume, search time, speed, acceleration, lane-change frequency, and distracted time of the cruising car. The multiple linear regression model was also established to compare with proportional hazard-based duration model results. The results indicated that between 9 and 56 percent of the traffic was cruising for parking, and the average search time was about 6.03 min. The low-speed, volume, high acceleration frequency, and lane-change times of cruising cars have a negative effect on shortening travel time of the normal traffic flow. Conversely, high-speed of cruising cars has a positive effect on shortening travel time of traffic flow. Moreover, travel time changes in varying degrees due to various factors. Under postulated conditions, the model can be used to estimate the travel time. It is hoped that this study will contribute to improve the planning and management of cruising for parking.

Section: Discussionmentioning

confidence: 99%

Impact of Cruising for Parking on Travel Time of Traffic Flow

Zhu

Chen

et al. 2020

“…The relationships among the indicators should be considered to determine the fleet size of taxis. The weight and importance ranking also need to be cleared up [21]. The regression tree model used a simple and comprehensible structure for data analysis, and thus, can be used for classification, reorganization, decision making, and developing prediction models.…”

Section: Regression Tree Modelmentioning

confidence: 99%

A Dynamic Adjustment Model of Cruising Taxicab Fleet Size Combined the Operating and Flied Survey Data

Yang

et al. 2020

Due to the lack of adjustment index systems for taxi fleet sizes in China, this paper used the taxi operating datasets from Ningbo City and established a regression tree model to consider the endogenous indicators that affect taxi fleet sizes. Then, a dynamic adjustment mechanism of taxi fleet sizes was proposed by combining the exogenous and endogenous indicators. The importance of the exogenous and endogenous indicators was sorted using the Delphi method. The threshold value of each indicator was also given. The results indicated that (1) in the three-layer structure of the regression tree model, the mileage utilization had the strongest effect on the fleet size of taxis, and the F statistic was 63.73; followed by the average daily revenue of a single taxi, the average waiting time to catch a single taxi, the average operating time of a single taxi, and the revenue per 100 km. The overall accuracy of the model was found to be valid. (2) When the mileage utilization was less than 0.6179 and the average daily revenue of a single taxi was less than 798.38 Yuan, the fleet size of cruising taxis was surplus and should be reduced by 362 vehicles. (3) When the mileage utilization was more than 0.6774 and the average waiting time to catch a single taxi was more than 259.09 s, the fleet size of cruising taxis was insufficient, and we suggest an increase of 463 taxis.Sustainability 2020, 12, 2776 2 of 18 demand is more than the supply, it is difficult and inconvenient for the passengers to take a taxi for the trip. Otherwise, if the number of taxicabs is too high, negative effects, including traffic congestion, increases in passenger trip costs, and decreased taxi driver revenue, are generated.How to regulate a reasonable taxi fleet size to satisfy the passenger demand without sacrificing the income of taxi drivers is an important premise of taxi fleet size adjustment. Meanwhile, both the operational efficiency and service level also need to be guaranteed. Unfortunately, accurate threshold values of indicators for adjusting the fleet size of taxicabs do not exist. It is necessary for the local government to establish a reasonable adjustment method for taxi fleet size and to alleviate the conflicts between the cruising taxis and Didi and Uber. A reasonable dynamic adjustment of the taxi capacity scale can increase the mileage utilization, alleviate the traffic congestion, and reduce the carbon emissions of taxicabs. Sustainable taxi industry is an important guarantee for promoting the green development of urban traffic. Additionally, as intra-city express service develop rapidly in China, taxicabs also tried to provide express, document delivery, baggage service and other freight services in city distribution. In order to gain more money, the taxicabs, such as Shanghai city already have the permit to provide the light freight services. Therefore, taxicabs as a new type of freight service in city distribution, also need to be focused on the reasonable fleet size.Many scholars studied the forecasting and monitoring of taxi ...

“…Mannini [15] used FCD data (Floating Car Data) of the detection vehicle to identify cruising vehicles and model their cruising time. Zhao et al [16] showed a station-oriented clustering analysis on ridership patterns in subway systems based on smart card date, and the results contributed to subway station ridership forecasting and provide theoretical basis for schedule making and adjustment.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of Risk and Benefit on the Suppliers’ and Managers’ Intention of Shared Parking in Residential Areas

Jin

Yang

et al. 2019

Shared parking is not commonly applied in residential areas. The reason is that parking suppliers and managers believe that there are many uncertainties and conflicts in obtaining sharing benefits and taking sharing risks. To increase their acceptance of shared parking in residential areas, risk and benefit factors were identified by an influential analysis and a questionnaire survey. A research framework based on the structural equation model was developed to analyze the relationship between shared-parking risks, shared-parking benefits, management pressure, and intentions of parking suppliers and managers. The results showed that, to parking suppliers, the risks of shared parking have the largest effect on suppliers' intention to apply shared parking by a standardized coefficient of −0.85, followed by the benefits of shared parking (0.29), and management pressures (−0.14). To the parking managers, management pressures have the largest effect on managers' intention to apply shared parking by a standardized coefficient of −0.74, followed by the benefits of shared parking (0.52) and risks of shared parking (−0.46). These results can help in increasing parking suppliers' and managers' acceptance of shared parking in residential areas.