Transfer Trips Research Articles

A parallel set-based model on the shortest travel time in long-distance transportation systems

The shortest travel time in long-distance transportation systems (LDTS) is a key indicator for measuring regional connectivity and mapping accessibility at national and global levels. For traditional methods, it is a great challenge to calculate the shortest travel time with millions of origin-destination pairs and evaluate the overall performance. To fill this technique gap, this study proposed a novel model to get all the trips with different numbers of transfers through a series of set-based methods and then calculate all the shortest travel time between stops. The set-based model was tested for calculating the shortest travel time in LDTS of China. Using the optimization algorithm, we can significantly reduce the number of transfer trips that need to be calculated. For instance, the number of transfer trips decreased by 96.17%, 98.15%, and 79.02%, for conventional railway, high-speed railway, and air transportation, respectively, when the number of transfers was two. The set-based model can be extended to calculate the door-to-door travel time between places, for instance, mapping the fine-scale accessibility at the national level. Furthermore, we proved that this set-based model, proposed in this study, could also be parallelized and applied to any other LDTS in the General Transit Feed Specification format.

Understanding bikeshare mode as feeder to bus:A case study of Chicago’s Divvy system

The bike-share system gives a decent solution to the first- and last-mile problem. And it connects trip origins/destinations and transit stations. However, current research in this area focuses more on metrobikeshare pattern, and there are still substantial gaps in research in the area of bus-bikeshare pattern. Focusing on Chicago, this study uses Divvy’s location information data to explore the spatial and temporal characteristics of bus-bikeshare transfer trips. Besides, we examine the built environment factors affecting bus-bikeshare transfers. This study uses buffers and time limits to isolate bus-bikeshare transfer trips. Two recognition rules proposed are a maximum transfer time of 20 min and a maximum buffer distance of 100 m. The findings are listed as follows: (1) the average distance and duration of bus-bikeshare trips is 1740.95m and 548.11s; (2) the bus-bikeshare trips show characteristics in different areas, seasons and periods; (3) there is a positive and significant spatial autocorrelation for the bus-bikeshare trips; (4) The bus-bikeshare pattern is influenced by built environment factors in a manner distinct from the bikeshare-only pattern, exhibiting its own unique characteristics.

Modeling non-compensatory strategies on path choices in a complex urban rail transit network considering characteristics of transfer passengers and trips

The path choice behavior of passengers is highly affected by transfer across different lines in a complex urban rail transit network that provides passengers with multiple path alternatives. Hence, the design of a transfer station in terms of its layout and transfer paths is gaining impact on passengers’ path choices in an expanding urban rail transit network. A comprehensive understanding of passenger behavior is essential for transportation planning and management to better design transfer stations and network operations. A questionnaire survey is conducted for passengers’ path choices and perception of transfer stations. Based on the collected data, this study develops a joint path choice model that combines Multinomial Logit Model (MNL) and Constrained Multinomial Logit Model (CMNL) to capture both compensatory and non-compensatory behavior, especially the latter one, which has not been fully explored in the current literature. Further, a membership model is constructed to analyze the heterogeneity of passengers’ non-compensatory behavior on transfer walking time. Four behavioral segments are identified in regard to passenger and trip characteristics. The estimation results show that the proposed model outperforms the general MNL model and allows a more convincing explanation of urban rail transit passenger behavior. Several research findings are derived, including that transfer walking time is more negatively perceived by passengers without non-compensatory behavior. Moreover, in-vehicle travel time is likely to be a minor consideration for passengers with non-compensatory behavior. The proposed model and framework enable urban rail transit operators and planners to retrieve more precise prediction of passenger flows and enhance service quality.

Analysis and Prediction of Dockless Shared Bike Demand Evolving Around Urban Rail Transit Stations: Case Study in Shenzhen, China

The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.

Correlation between the built environment and dockless bike-sharing trips connecting to urban metro stations

The influence of the built environment on dockless bike-sharing (DBS) trips connecting to urban metro stations has always been a significant problem for planners. However, the evidence for correlations between microscale built-environment factors and DBS-metro transfer trips remains inconclusive. To address this, a framework, augmented by big data, is formulated to analyze the correlation of built environment with DBS–metro transfer trips from the macroscopic and microscopic views, considering Beijing as a case study. The trip density and cycling speed are calculated based on 11,120,676 pieces of DBS data and then used to represent the characteristic of DBS-metro transfer trips in a multiple linear regression model. Furthermore, a novel method is proposed to determine the built-environment sampling area around a station by its corresponding DBS travel distances. Accordingly, 6 microscale built-environment factors are extracted from street-view images using deep learning and integrated into the analysis model, together with 14 macroscale built-environment factors and 8 potential influencing factors of socioeconomic attributes and metro station attributes. The results reveal the significant positive influence of greenery and presence of barriers on trip density and cycling speed. Additionally, presence of streetlights is found to be negatively correlated with both trip density and cycling speed. Presence of signals is also found to have an influence on DBS-metro transfer trips, but it only negatively impacts trip density.

Modeling Non-Compensatory Strategies on Path Choice Decisions in a Complex Metro Network Considering Characteristics of Transfer Passengers and Trips

Modeling Non-Compensatory Strategies on Path Choice Decisions in a Complex Metro Network Considering Characteristics of Transfer Passengers and Trips

Формирование системы транспортно-пересадочных узлов в условиях изменения пассажиропотока

The paper proposes a quantitative method for determining the optimal location of transport interchange hubs (TH), based on identification of effective transfer hubs, which allows predicting the location of the TH system with sufficient accuracy according to the criterion of reducing the average duration of trips around the city. The method is based on the heuristic algorithm that allows you to determine the optimal direct and transfer routes (with one, two or more transfers) and identify effective transfer nodes. In such nodes, the TH should be created. The method was tested on the example of the Samara city public transport system. The TH system is proposed, which allows to reduce the average travel time on the urban transport network by reducing the time of transfer trips. Regardless of the size of passenger traffic, the maximum reduction in the average duration of a trip in Samara due to the creation of a TH system is six minutes. The fluctuation of passenger traffic and its influence on the choice of TH is investigated. The dependence of the number of TH, the construction of which makes it possible to achieve marginal reduction in the average duration of a trip around the city due to the formation of TH system on passenger traffic is obtained.

Identification of Metro-Bikeshare Transfer Trip Chains by Matching Docked Bikeshare and Metro Smartcards

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics.

Analyzing the accessibility of subway stations for transport-vulnerable population segments in Seoul: Case of bus-to-subway transfer

With the increasing interest in smart card data and transport-vulnerable population segments, data-based analysis has become increasingly important. This study aimed to design transfer trips by using smart card usage data to analyze vulnerable transfer routes for each user group. After merging smart card and basic information data such as stops, stations, and facilities, we constructed a transfer dataset of individual users according to the specified transfer time ranges and conditions. The transfer analysis targets were limited to transfers between the bus and subway, and transfer patterns for each user group (general population, youth, children, elderly, and disabled) were analyzed by date and time. The analyzed populations were then divided into general and vulnerable groups, and vulnerable transfer routes were identified. The actual transfer time differences between the general and vulnerable groups were then analyzed. We directly measured the expected transfer time differences between these groups based on map data and different walking speeds. We then calculated the differences between the expected and actual transfer times and analyzed the correlation between the number and location of accessible facilities in subway stations. We hope that this analysis will be useful for guiding the installation of accessible facilities in subway stations.

The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space

Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored.First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling.

Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Dockless bike-sharing is emerging as a convenient transfer mode for metros. The riding distances of bike-sharing to or from metro stations are defined as transfer distances between dockless bike-sharing systems and metros, which determine the service coverages of metro stations. However, the transfer distances have rarely been studied and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variations of transfer distances between dockless bike-sharing systems and metros. First, a catchment method was proposed to identify bike-sharing transfer trips. Then, the Mobike trip data, metro smartcard data, and built environment data in Shanghai were utilized to calculate the transfer distances and travel-related and built environment variables. Next, a multicollinearity test, stepwise regression, and spatial autocorrelation test were conducted to select the best explanatory variables. Finally, a geographically weighted regression model was adopted to examine the spatially varying relationships between the 85th percentile transfer distances and selected explanatory variables at different metro stations. The results show that the transfer distances are correlated with the daily metro ridership, daily bike-sharing ridership, population density, parking lot density, footway density, percentage of tourism attraction, distance from CBD, and bus stop density around metro stations. Besides, the effects of the explanatory variables on transfer distances vary across space. Generally, most variables have greater effects on transfer distances in the city suburbs. This study can help governments and operators expand the service coverage of metro stations and facilitate the integration of dockless bike-sharing and metros.

Real scale measurements of yacht’s mast accelerations

ABSTRACT Dismasting is one of the most serious hazards for sailing boats. This event is mainly due to structural defects, poor maintenance or adverse weather conditions. For what the last aspect is concerned, it may be associated to two types of loads: the direct action of the wind on sails and accelerations caused by ship motions. The aim of this paper is to focus on the accelerations induced on the mast by ship motions using seakeeping analysis validated by real scale measurements carried out on two sailing yachts during their transfer trips from South Africa to Italy. After measurements, the recorded data have been analyzed, reordered and related to the weather conditions encountered during the transfer voyage. Then seakeeping calculations have been carried out in order to compare numerical prediction with experimental values. Finally, acceleration values to be considered for preliminary mast scantling, were derived.

Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

Circuity in China's high-speed-rail network

This study investigates the circuity of China's high-speed-rail (HSR) network from 2014 to 2016 and analyzes the network performance. The concept of circuity has been redefined in terms of travel time so that various speed levels of HSR lines can be measured systematically. In this study, circuity is redefined as the ratio of actual travel time to ideal travel time. By using actual HSR trip records, the influence of passenger demand and the circuity of transfer trips have been examined. At the node level, we find that the circuity of principal stations has significantly decreased overall. For stations with lower circuity, transfer trips from/to them tend to be more circuitous. Although stations along the intercity rail lines show higher circuity, they contribute to regional coverage and connectivity. Finally, we find that circuity tends to increase with a decreasing passenger flow for OD pairs within a certain distance range, and the passenger flow may decline as the OD distance increases.

Measuring the Destination Accessibility of Cycling Transfer Trips in Metro Station Areas: A Big Data Approach.

Cycling is a green, sustainable, and healthy choice for transportation that has been widely advocated worldwide in recent years. It can also encourage the use of public transit by solving the “last-mile” issue, because transit passengers can cycle to and from transit stations to achieve a combination of speed and flexibility. Cycling as a transfer mode has been shown to be affected by various built environment characteristics, such as the urban density, land-use mix, and destination accessibility, that is, the ease with which cyclists can reach their destinations. However, cycling destination accessibility is loosely defined in the literature and the methods of assessing cycling accessibility is often assumed to be equivalent to walking accessibility using the same decay curves, such as the negative exponential function, which ignores the competitive relationship between cycling and walking within a short distance range around transit stations. In this study, we aim to fill the above gap by measuring the cycling destination accessibility of metro station areas using data from more than three million bicycle-metro transfer trips from a dockless bicycle-sharing program in Shenzhen, China. We found that the frequency of bicycle-metro trips has a positive association with a trip distance of 500 m or less and a negative association with a trip distance beyond 500 m. A new cycling accessibility metric with a lognormal distribution decay curve was developed by considering the distance decay characteristics and cycling’s competition with walking. The new accessibility model outperformed the traditional model with an exponential decay function, or that without a distance decay function, in predicting the frequency of bicycle-metro trips. Hence, to promote bicycle-metro integration, urban planners and government agencies should carefully consider the destination accessibility of metro station areas.

Exploring the catchment area of an urban railway station by using transit card data: Case study in Seoul

To enhance transit ridership, Seoul introduced a transfer discount fare scheme that uses an automated fare collection system in Seoul 2004. The transfer discount fare system records all transfer information between rail and buses in transit smartcard data, which enabled us to explore an urban railway station's catchment area. In this study, we examined the geographic distribution of rail-to-bus transfer trips and their characteristics by using transit smartcard data. Mokdong station in Seoul was used as a case study to demonstrate the benefits of data mining for the depiction and easy evaluation of a station's catchment area. The results showed that the average transfer passenger traveled 1.7 km with five bus stops after boarding to access the business district during the morning peak hour. The cumulative distribution of alighting passengers by bus route helped with inferring the shape and size of the urban railway station's catchment area in each direction and depending on the time of day. We found that reliable transfer travel data constitute valuable information for evaluating an urban railway station's catchment area with respect to the type of land use and will help transit agencies with providing better transit services in terms of enhanced accessibility by changing bus headways and routes, as well as land use planners with evaluating transit-oriented development based on the expanded concept of a metro station's catchment area.

Urgency of Licensing Restriction in Joint Venture Companies Related to TKDN

Most companies that market their products in Indonesia in order to pass TKDN using the concept of Joint Venture agreements (PMA) often the parties working together are unbalanced in real terms the shares of foreign owners are greater than domestic shareholders. In such conditions the strong parties tend to impose their will on the weaker party. Therefore, according to the principle of freedom of contact in relation to the free market, in fully contracting is an affair of the parties, however legal protection and public interest are therefore required from government interference in the form of regulation or restrictions. The restrictions in regulating technology transfer from developed countries to developing countries aim to protect the interests of countries that divert technology because the inventor of the technology is considered to have made maximum efforts to find related technology but on the other hand the state is also obliged to protect and improve the welfare of its citizens from that, restrictions on patent licenses are needed so that the TKD is truly "real" and does not reduce the incoming FDI.Based on this, the authors formulated a number of issues namely: Why are restrictions on patent licenses needed and What are the legal consequences of limiting patent licenses . The results of the discussion show that the transfer of technology is needed for developing countries needed to advance their products in the era of globalization so that arrangements for it are needed so that in case of cooperation there is no inequality. And, the role of law in the policy of technology transfer to transform agrarian societies into industrialist societies. Here there is a dilemmatic situation on the one hand the acceleration of mastery of technology including the acceleration of development needs to be done by being open to the owners of capital and technology (which generally comes from developed countries), while on the other hand we still have to maintain national interests. Here is related to the authority of the state to regulate the process of technology transfer. In this global era, after the WTO agreement was reached, which was linked to 2 (two) technology transfer agendas, namely TRIMS and TRIPS. Foreign technology protection was very much needed in the context of foreign investment.

Alighting stop determination using two-step algorithms in bus transit systems

Smart cards of most bus transit system only record boarding stops of passengers, which hinders direct exaction of alighting stops. In this paper, we associate multi-source information to derive transit OD matrix. Passengers are segmented into regular and irregular groups using K-means clustering based on trip frequency. In the first step, a deterministic model such as the trip chain analysis is respectively conducted on the two groups of passengers. For records that fail to be detected by deterministic rules, machine learning algorithms are developed to determine the alighting stop in the second step. All the destinations can be detected by the two-step algorithms, while only partial records can be determined in the past. Transfer trip recognition helps to distinguish transfer trips from single trips. This research develops a set of simple and applicable methodologies in alighting stop determination, which can be applicable more broadly to other transit datasets.

Tuning interconnection relays using a multi-agent system to detect weak infeed conditions

In this paper, a multi-agent system (MAS) is used to enhance the performance of protection under dynamic operating conditions that result in a weak source contribution. A traditional relay setting that is established for the worst-case scenario is not suitable for systems with a wide operating range, mainly due to the presence of non-regulated sources. A MAS is proposed that operates on-line to modify relay settings in response to topological changes due to the output of lines, loads or generation, and identifies low contributions from sources. The gang architecture for the relay coordination is simulated in Matlab/Simulink and controlled using Jade. Matlab enables modeling that integrates the diverse dynamics of the elements of the electrical network, and Jade allows for a multi-agent environment that facilitates the different actions that are activated in the network. The MAS architecture improves the relay performance under fault conditions by detecting weak infeed conditions caused by both non-regulated sources and low-level conventional sources. The on-line coordination between interconnection relays and line relays uses the MAS architecture to send transfer trips, and uses non-conventional time curves to improve the operation times. The results show that the MAS improves the sensitivity and operation times of the interconnection relays.

Evaluation of Transfer Efficiency between Bus and Subway based on Data Envelopment Analysis using Smart Card Data

The government of Seoul has been operating the automatic fare collection system based on the smart card since 2004. The smart card data in Seoul consist of 15 million instances of individual transit information per day, providing 99% of transit users’ trips. This study provides information about the efficiency of transfer stations in Seoul. The purpose of this study was to estimate the relative efficiency of the transfer stations between bus and subway using smart card data and suggest the improvement strategies for achieving the optimal efficiency. The transfer efficiency was estimated by using the Data Envelopment Analysis (DEA) model, and Tobit regression analysis was conducted to identify the factors that influence transfer efficiency. The DEA model showed that the efficiency scores of 32 major stations were estimated to be 0.557 on average. The transfer efficiency scores of these stations were analyzed to be proportional to the number of transfer trips and the transfer rate of the station. In the external factor analysis, we selected two socioeconomic variables, i.e., population and the number of companies. The external factor analysis indicated that the DEA model produced reasonable results for evaluating transfer efficiency.