Travel Chain Research Articles

The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model

With urban expansion and traffic environment improvement, travel chains continue to grow, and the combination of travel purposes and modes becomes more complex. The promotion of mobility as a service (MaaS) has positive effects on facilitating the public transport traffic environment. However, public transport service optimization requires an accurate understanding of the travel environment, selection preferences, demand prediction, and systematic dispatch. Our study focused on the relationship between the trip-chain complexity environment and travel intention, combining the Theory of Planned Behavior (TPB) with travelers' preferences to construct a bounded rationality theory. First, this study used K-means clustering to transform the characteristics of the travel trip chain into the complexity of the trip chain. Then, based on the partial least squares structural equation model (PLS-SEM) and the generalized ordered Logit model, a mixed-selection model was established. Finally, the travel intention of PLS-SEM was compared with the travel sharing rate of the generalized ordered Logit model to determine the trip-chain complexity effects for different public transport modes. The results showed that (1) the proposed model, which transformed travel-chain characteristics into travel-chain complexity using K-means clustering and adopted a bounded rationality perspective, had the best fit and was the most effective with comparison to the previous prediction approaches. (2) Compared with service quality, trip-chain complexity negatively affected the intention of using public transport in a wider range of indirect paths. Gender, vehicle ownership, and with children/without children had significant moderating effects on certain paths of the SEM. (3) The research results obtained by PLS-SEM indicated that when travelers were more willing to travel by subway, the subway travel sharing rate corresponding to the generalized ordered Logit model was only 21.25-43.49%. Similarly, the sharing rate of travel by bus was only 32-44% as travelers were more willing to travel by bus obtained from PLS-SEM. Therefore, it is necessary to combine the qualitative results of PLS-SEM with the quantitative results of generalized ordered Logit. Moreover, when service quality, preferences, and subjective norms were based on the mean value, with each increase in trip-chain complexity, the subway travel sharing rate was reduced by 3.89-8.30%, while the bus travel sharing rate was reduced by 4.63-6.03%.

Electric Vehicle Charging Load Demand Forecasting Model based on Spatial and Temporal Characteristics

This paper proposes a method to predict the charging load demand of electric vehicles considering the spatial and temporal distribution characteristics of users’ travel. Based on the central limit theorem and travel chain theory, the temporal distribution of the charging load of different functional sites is simulated by the Monte Carlo method and calculated by combining NHTS2017 data to find the total daily charging load of electric private vehicles. The results show that in the case of disorderly charging, the charging loads of different areas have more obvious temporal characteristics, and the charging service can be optimized according to this charging load characteristic.

Research on Integral Traffic-sharing Platforms Based on Block Chain

Starting from the catering industry, using the decentralized characteristics of block chain technology, to research the integrated traffic sharing platform, to provide a guarantee for the actions of improving customer loyalty, preserving and obtaining customer traffic. Build a self-driving shared traffic distribution system with the help of block chain technology, reduce the risk of credit over-issuance, and establish an efficient and safe benefit protection mechanism through smart contracts. Research results: The application of block chain technology to the catering industry can promote decentralization, build a trust mechanism, realize the value of data, and strengthen property rights protection, but it is difficult to implement the project, requiring a large amount of financial and technical support. The introduction of block chain technology into the construction of the integrated traffic-sharing platform can provide merchants with traffic opportunities and satisfy consumers' personalized consumption motivation. The complete system, science and technology integrated travel chain has become a brand barrier and has been the escort for the catering consumption industry.

An exploratory study of MaaS in Shandong

As a new service concept, Maas (mobility as a service) was initially defined as providing an available travel service mode for residents in low-density areas and low-income populations. Maas was first implemented in western countries to study the situation of low density and low salary in various countries[1]. In contrast, in China, because the complex national conditions are diametrically opposite to the low density and low salary situation in the west, China urgently needs to explore a new paradigm of the Sinicization of Maas[2]. Based on the national conditions of high density and high car ownership, this research conducted an exploratory research on the problem of private car owners' changing from owning vehicles to owning transportation services, in order to explore how to add private cars to the Maas public travel chain.

LSTM-Based Transformer for Transfer Passenger Flow Forecasting between Transportation Integrated Hubs in Urban Agglomeration

A crucial component of multimodal transportation networks and long-distance travel chains is the forecasting of transfer passenger flow between integrated hubs in urban agglomerations, particularly during periods of high passenger flow or unusual weather. Deep learning is better suited to managing massive amounts of traffic data and predicting extended time series. In order to solve the problem of gradient explosion or gradient disappearance that recurrent neural networks are prone to when dealing with long time sequences, this study used a transformer prediction model to estimate short-term transfer passenger flow between two integrated hubs in an urban agglomeration and a long short-term memory network to incorporate previous historical data. The experimental analysis uses two sets of transfer passenger data from the Beijing-Tianjin-Hebei urban agglomeration, collected every 30 min in May 2021 on the transfer corridors between an airport and a high-speed railway station. The findings demonstrate the high adaptability and good performance of the suggested model in passenger flow forecasting. The suggested model and forecasting outcomes assist management in making capacity adjustments in time to correspond with changes, enhance the effectiveness of multimodal transportation systems in urban agglomerations and significantly enhance the service of long-distance multimodal passenger travel.

Dividing Journeys in Milestones – A passenger (agent) trajectory reference

A journey is more than the connection between starting point and destination. Each person, each cargo passes through several stations until it reaches the desired destination. These stations can pass through similar processes despite the different transport modes and their dimensions (cf. Engler et al.). While the passenger waits at the airport security for the screening of the hand luggage, the cargo container at the port is also checked by customs officials. Both processes have the same objective, namely to investigate the traveling person and the container, for illicit items. It is evident that the start and end points of these processes should be given a similar designation. Dividing journeys in milestones frames a concept for research of interdependencies and interactions between planning and execution of travel chains from the infrastructure owner's and traveler's points of view.

Construction of Commuters’ Multi-Mode Choice Model Based on Public Transport Operation Data

Travel mode selection is a crucial aspect of traffic distribution and forecasting in a comprehensive transportation system, which has significant implications for resource allocation and optimal management. As commuters are the main part of urban travel, studying the factors that affect their choice of transport mode plays a crucial role in urban traffic management and planning. Based on public transport operation data, a travel chain is created by identifying boarding stations, alighting stations, and transfer behaviors, and includes detailed travel information. The regression and correlation coefficients of departures and arrivals at stations are confirmed to be 0.98 and 0.92 in the presented data, indicating the viability of the recognition method. Then, multiple travel modes are identified based on the origin and destination, and the proportion of mode selection is determined by the actual travel chain. Using maximum likelihood estimation (MLS) and NLOGIT software, the random parameter logit (RPL) mode is used to estimate the relationship between travel mode selection and characteristic variables such as travel time, distance, cost, comfort, walking distance, and waiting time. The results indicate that walking distance, travel distance, and comfort have a greater influence on travel choice, and that walking distance is a random parameter with a normal distribution, reflecting the diversity of commuters. In addition, this paper discusses the influence degree of the change of characteristic variables of a transport mode on the choice between it and other modes. These results can be used as reference for relevant departments to make measures to improve the overall efficiency of the urban transportation system.

Power restoration model for distribution networks after extreme rainstorm disasters based on electric vehicle travel characteristics

Improving the resilience of the distribution grid to extreme rainstorms can effectively reduce the resulting outage losses. Based on the massive number of electric vehicles (EV) with objective capacity in the distribution network environment, it is essential to study the discharge of EVs to make up for the shortage load in the distribution network’s post-disaster recovery. In this paper, we first establish a coupling network integrating the distribution grid and road network, simultaneously coupling information from different levels. Then establish a model describing the characteristics of EV travel behavior based on the travel chain and probability distribution function to portray the spatial distribution of EVs in the time dimension. Then establish a model of EVs in this area and cross-area EV participating in discharge response based on the area after EV clustering. Also, establish the extreme storm weather affected EV discharge willingness model to describe the uncertainties of EVs. Finally, two faults set in the IEEE33 node are simulated and verified, and the model description and analysis of the influencing factors of EV participation in discharge are given.

A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.

Influence of COVID-19 on air travel - A scenario study toward future trusted aviation

This paper develops three scenarios for the aviation industry's recovery from COVID-19 until 2030 by utilizing the scenario methodology. Besides the short- and mid-term pandemic development, the study takes into account the industry's adaptation to changes in the market environment, e.g., toward sustainability and hygiene requirements. The resulting scenarios include the expected point in time of full air traffic recovery to pre-crisis levels. Subsequent implications suggest that most COVID-19-related hygiene measures along the travel chain disappear after the pandemic is contained. Some measures might serve as a differentiator between airline business models, while others are expected to become a new standard. Implications for environmental awareness and resulting operational and technical measures include changes in society's attitude toward traveling post-pandemic, especially in light of varying levels of environmental awareness. The presented scenarios help to identify the range of plausible development paths, thus building the basis for future model-based research.

Transport experiences of people with disabilities during learnerships.

Transport is a known national barrier for people with disabilities in South Africa. It is similarly identified as a barrier in learnerships and economic opportunity programmes. This article discusses the extent to which transport is a barrier during learnerships for students with disabilities. The Department of Transport administered an online evaluation questionnaire to a random sample of students with disabilities. Results were coded in terms of 'barriers to access' and 'barriers to participation'. The data were organised into themes. The collated evidence is discussed in this article. The findings demonstrated that transport barriers were present in different modes of transport and different parts of the travel chain. However, the findings also demonstrated the negative impact of transport on the learnership experience and economic opportunities. The findings indicated that inaccessible transport is an integral cause of learnership incompletion for students with disabilities, where the universal accessibility of both transport and the built environment are a prerequisite need. Most students with disabilities reported that transport was not a barrier to learnership participation or that problems with transport could be resolved. Nevertheless, it was one of the identified barriers that negatively affected learnership participation experiences. It was a significant barrier to learnership completion for students with the most severe experience of disability. The sample consisted of only 32 students and a high number of unspecified responses. Evidence from other studies indicates that transport for all persons with disabilities remains a barrier warranting further examination, because public transport has remained inaccessible for over 23 years. Further research is required to verify this study and to investigate learnership cost-benefit for all students.

Airport Access Mode Choice: Analysis of Passengers’ Behavior in European Countries

Transportation systems require many challenges in providing seamless door-to-door mobility. The main initiatives are encouraging a shift from private to other transport modes by providing a fully integrated multimodal service in which the coordination and data sharing among different stakeholders are required. The idea of this paper is to analyze the mode choice, as well as the variables that affect the travelers’ airport access mode choice. For that purpose, we used multinomial logistics (MNL) regression to determine probability of mode choice given various multimodal chain alternatives. The inputs of the proposed model were based on the answers from the participants of the online survey which was disseminated in Europe. Through more than 2000 answers to the survey, we collected the data related to the factors that influence the airport access mode choice, travelers’ attitude, motives for traveling, as well as the socio-demographics of participants. Afterwards, we investigated the influence of the main factors that have an impact on the non-coordination in the multimodal travel chain. The obtained results highlight the impact of the factors “reliability” and “waiting time” in making mode choice.

Sizing and Locating Planning of EV Centralized-Battery-Charging-Station Considering Battery Logistics System

This article investigates the optimal planning of centralized charging and multipoint distribution network that includes centralized battery charging stations (CBCSs) and battery distribution stations (BDSs), which considers the battery logistics system. Based on the analysis of the characteristics of travel chains of swapping electric vehicles (SEVs), a time-space load forecasting model of SEVs is proposed. Then, based on function and characteristic analysis, operation models of CBCS, BDS, and the battery logistics system are built. Afterward, the sizing models of CBCSs and BDSs are established. Then, a double-level planning model of CBCSs considering battery logistics system is proposed. In the first level planning, the sizes and locations of CBCSs and BDSs are planned, and in the second-level planning, the economy of battery logistics company is optimized. Finally, the CPLEX toolbox and the Bellman–Ford algorithm are used to solve the planning model. Taking the actual situation of a certain region as the planning area, the feasibility and effectiveness of the method proposed in this article are verified.

Beyond Start and End Points: Accessibility of Railway Stations on a Multi-Spatial Scale

This paper presents results and policy implications from RAISE-IT, an EU Connecting Europe Facility (CEF) funded project. It examines the integration of long-distance rail with local and regional transport networks at key urban nodes along the Rhine-Alpine Corridor, one of the nine Trans-European Core Network Corridors (CNCs). It is crucial to foster seamless and suitable passenger transport solutions to achieve emissions goals and support the decarbonization process. Consequently, the whole travel chain needs to be considered and good interconnections with other transport modes are needed. For this reason, RAISE-IT explored multi-spatial scale accessibility. It considered three spatial scales (local, regional, and corridor-wide) and looked into multiple travel distances and needs along the corridor for accessing and minimizing the gap between highly and less accessible railway stations with reference to multi-scale travel patterns. This approach revealed overlapping issues between cross-regional and local decision makers and facilitated opportunities to discuss challenges and areas for improvement. The results formed the basis for guidelines on how to improve the accessibility of urban nodes and the quality of services and connections provided at railway stations. This paper discusses potential transferability aspects and recommendations for policy implications from RAISE-IT for railway stations and urban nodes in other CNCs.

Research on Marketing Strategy of Railway Passenger Travel Behaviour Analysis in Competitive Section

The deep mining of passengers’ travel data can identify competitive segments and gain insights into passengers’ characteristics and differentiated demands. This can not only effectively support precise marketing strategy adjustment of railway transport but also improve its competitiveness in the passenger transportation market. In this paper, hidden railway travel behaviour is introduced and integrated with railway travel behaviour to create a complete passenger travel chain, based on existing distance-based competitive segment recognition methods. The loyalty index values of passengers are calculated using this travel chain to identify competitive segments. Furthermore, passenger classification and grouping currently ignore social relationships as well as personal travel characteristics. Therefore, a novel passenger grouping method is proposed; it integrates individuals’ travel characteristics and social relations. Individual travel labels are created for travellers based on their travel data. Social relation topologies, such as ticketing relation, the relation of travelling together, and benefit relation via point redemption, can be extracted using these labels. Social relation traits can be retrieved using graph attention networks and multigraph fusion. Finally, travellers are categorised based on their individual travel characteristics. As an example, and a case study, the grouping of Guangzhou–Shanghai passengers in 2020 is taken which shows that the suggested method has the potential to improve both the precision and the feasibility of grouping railway passengers. As a result, new ideas for passenger grouping in railway marketing might be offered.

A Two-Phase, Joint-Commuting Model for Primary and Secondary Schools Considering Parking Sharing.

In light of the traffic congestion and traffic environment problems around schools that are caused by students commuting by car, this paper explores an efficient and feasible student commuting travel plan. Based on the ideas of “public–private cooperation” and “parking sharing”, combined with the characteristics of the family travel chain during the commuting period, a joint-commuting model of “private car and school bus” is creatively proposed. On the basis of considering the travel cost of parents and the operating cost of school bus, a two-phase commuting travel model for primary and secondary schools is proposed, and an algorithm is designed. The validity of the model is verified by an example and sensitivity analysis. The results show that the total time cost can be reduced by 23.33% when the private-car commuting mode is converted to the joint-commuting model. Among the results, we found that the driving time of a private car in the school commuting phase can be reduced by 23.36%, the dwell time can be reduced by 92.29%, and the driving time in the work and home phase can be reduced by 7.44%. Compared with the school-bus commuting mode, the school-bus time cost of joint commuting can be reduced by 54.88%. In addition, by analyzing the impact of various factors on the objective function and vehicle emissions, it can be seen that staggered commuting to school, regulating regional traffic volume, increasing parking spaces, and improving the utilization of parking spaces can effectively reduce the operating time cost of vehicles and exhaust emissions. The joint-commuting model proposed in this paper considers the balance between service level and resource consumption. While meeting the door-to-door travel needs of students, it can effectively reduce the travel costs of parents and school-bus operation costs, and it can alleviate traffic congestion around schools and reduce the impact on the environment.

Traveling More Independently: A Study on the Diverse Needs and Challenges of People with Visual or Mobility Impairments in Unfamiliar Indoor Environments

It is much more difficult for people with visual or mobility impairments to prepare for a trip or visit unfamiliar places than it is for people without disabilities. In addition to the usual travel arrangements, one needs to know if the various parts of the travel chain are accessible. To the best of our knowledge, there is no previous work that examines in depth travel behavior for indoor environments for both trip planning and execution, highlighting the special needs of people with low vision, blindness, or mobility impairments (MIs). In this article, we present a survey of 125 participants with blindness, low vision, and MIs. We investigate how mobile they are, what strategies they use to prepare a journey to an unknown building, how they orient themselves there, and what materials they use. For all three groups, our results provide insights into the problem space of the specific information needs when planning and executing a trip. We found that most of our participants have specific mobility problems depending on their disability. Feedback from the participants reveals that there is a large information gap, especially for orientation in buildings, regarding availability of high-quality digital, tactile, and printable indoor maps; accessibility of buildings; and mobility supporting systems. In particular, there is a lack of available and high-quality indoor maps. Our analysis also points out that the specific needs differ for the three groups. Besides the expected between-group differences, large in-group differences can also be found. The current article is an expanded version of earlier work [ 18 ] augmented by data of people with MIs.

A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification

It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation.

Optimal Regulation Strategy of Electric Vehicle Charging and Discharging Based on Dynamic Regional Dispatching Price

With the continuous and rapid growth of electric vehicle (EV) ownership, a large number of disorderly charging loads accessed to the regional grid may lead to problems such as widening of the peak-to-valley load difference and deterioration of power quality. This study proposes an optimal EV charging and discharging regulation strategy based on dynamic regional dispatching price to give full play to the EV vehicle-to-grid (V2G) regulation potential and reasonably regulate EV charging and discharging behavior to ensure safe, high-quality, and economic operation of the grid. First, the EV travel chain model is constructed according to the travel behavior pattern of EV users to obtain the spatial and temporal distribution characteristics of EV charging and discharging loads. Second, considering the difference in regional grid operation states, a dynamic update strategy of regional dispatching price integrating power grid spatial and temporal information is proposed. Furthermore, considering the characteristics of EV mobile energy storage and user demand, an EV V2G optimal regulation model that takes into account the interests of distribution system operators, EV aggregators, and EV users is established to effectively guide the regulation of EV charging and discharging behavior. Finally, the simulation results show that the proposed strategy can effectively reduce the load deviation of each regional grid, operation cost of EV aggregators, and charging cost of EV users and significantly improve the safety and economy of the power system.

Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach

Commuters are the stable travel group for the public transportation (PT) service system. Accurately identifying the PT commuters is conducive to promoting PT service quality and development of urban sustainable transportation. This paper extracts individual PT travel chain information and constructs individual travel knowledge graphs of PT passengers based on the association matching algorithm and the theory of multilayer planning. A mixed dataset is formed by associating individual travel chains with travel survey data. Seven travel characteristic indicators regarding travel performance and spatiotemporal travel characteristics are extracted. The identification model of PT commuters is developed based on a three-layer backpropagation neural network (BPNN). The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. The evaluation indexes of overall accuracy and kappa coefficient of the constructed model are 94.5% and 87.9% separately. The results indicate that the model identification accuracy is acceptable, and the proposed characteristic indicators and systematic modelling procedure are effective. Then, the model performance is compared with the other five machine learning models further. The results confirm that the proposed model has a better identification accuracy and viability, and the model performance will improve with the increase of the sample size.