Intermodal Travel Research Articles

Identifying the Key Factors of Intermodal Travel Using Interpretative Ensemble Learning

Intermodal travel is considered an effective method for achieving sustainable urban transportation. Understanding the factors influencing intermodal travel is crucial. Due to the relatively small proportion of intermodal trips within cities, datasets are significantly imbalanced, leading to poor performance of traditional logit models. In this paper, we develop a novel interpretable ensemble learning (IEL) model to identify key factors through voting by five types of machine learning models. We test our model on two datasets with different numbers of features. The results show that travel duration, travel distance, vehicle ownership, and distance to the nearest metro station are the key factors influencing intermodal travel, cumulatively contributing nearly 70% in the JDS2021 dataset with 14 features and nearly 80% in the SHS2019 dataset with 8 features. Furthermore, we analyze the interpretability of our model and compare it with the logit model. Our model enriches the methodology for modeling intermodal travel behavior.

Analysis of passenger perception heterogeneity and differentiated service strategy for air-rail intermodal travel

Air-rail intermodal services (ARISs) represent a highly promising multimodal solution within the transportation sector. Nonetheless, various uncertainties and challenges persist across multiple dimensions of air-rail interline travel, with discrepancies in passenger perceptions being a notable aspect. In an effort to pinpoint the pivotal factors contributing to these disparities among distinct passenger profiles, this study employs the Structural Equation Modeling-Multiple Indicator Multiple Cause-Artificial Neural Network (SEM-MIMIC-ANN) methodology. This approach explores the impact of numerous attributes on passenger perceptions in the context of air-rail intermodal travel, leveraging questionnaire data gathered from Shijiazhuang multimodal passengers. Furthermore, the study utilizes the Classification and Regression Tree (CART) decision tree algorithm to categorize actual passengers into distinct characteristic groups. Subsequently, the perception levels of these diverse passenger groups are quantified through the calculation of comprehensive evaluation function values. In conclusion, taking into account the real-world conditions of air-rail interline travel, this research formulates a tailored service strategy aimed at enhancing the overall passenger experience.

Intermodal travel planning and decision support integrated with transportation and energy systems

The fast urbanization in China makes it all the more important to find sustainable solutions that are both comprehensive and energy-efficient. Because of its important role in lowering logistical expenses and pollutant emissions, intermodal transport is generally seen as an effective method of coordinating transportation operations, helping to address the growing economic and environmental issues. Considering the characteristics of a growing city, this article lays out a multi-criteria method for selecting which new initiatives for China's public transportation system should be prioritized. “Electric municipality bus,” “light rail system,” and “modernization to the current fleet and optimization” are the three enhancement initiatives that are outlined. Using transportation-related economic, social, and environmental sub-criteria, this research applies TOPSIS, an analytic hierarchy method and fuzzy approach for order preference by resemblance to ideal circumstance application, to prioritize transportation projects. The study aims to improve city life in Chongqing, China, by identifying the most environmentally friendly development projects. Applying the analytical hierarchy method (AHP), the relative importance of several sustainability criteria was established for use in making strategic decisions. The alternative projects for the given city have been ranked using the fuzzy TOPSIS approach. However, the investigated results show the supportive response of hybrid to environmental sustainability and vice versa for non-hybrid vehicles. However, the energy consumption in public transport remains a leading hurdle in sustainability across the three modes of transport: taxis, rail transit and buses. Finally, computer trains in public transport also surprisingly deal with environmental sustainability to keep the current & forthcoming generation from ecological harm. However, theoretical and empirical policy suggestions have been proposed to become clean & green shortly.

Visualizing the Influence of New Public Transport Infrastructure on Travel Times

Due to the global climate change and increasing traffic volumes in cities, a shift from individual to public and multimodal transport is aspired. Travel time is one of the most important aspects for many people when choosing their mode of transportation. This leads to the requirement that changes in travel times have to be considered when planning new public transport infrastructure. This research paper presents and compares different techniques for visualizing the impact of new lines in existing public transport networks on travel times. The general approach of simulating timetable data and calculating intermodal travel times considering public transport and walking is being applied to two current infrastructure projects in the city of Bonn and the surrounding region. The created maps generally aim to visualize the spread in travel times between existing and extended transportation networks discretized by different spatial units such as rectangles or postal code areas. In comparison to other common methods which typically require two maps for two different scenarios (e.g. in case of isochrones), our approach gives the opportunity to combine all relevant information within one map. It is also shown how to apply bivariate choropleth maps for displaying travel times and how to visualize improvements in the accessibility of multiple target points of interest at once.

Satisfaction of Istanbul Citizens with Urban Public Transportation

Transportation is one of the most challenging urban subsystems to transform in an environmentally friendly and futuristic way. Many city dwellers use a variety of modes of transportation. An efficient and sustainable urban transportation system must include many modes of transportation for a single trip. Intermodal combinations are essential for urban transportation efficiency. Public transportation and commuting are essential elements of multimodal travel. In urban areas, a mix of bicycles, vehicles, and public transportation is prevalent, while in rural areas, car, and public transportation are more prevalent. By examining the characteristics that lead customers to prefer water transportation over Metrobus and Marmaray, we hope to gain a better understanding of how the Asian and European sides of Istanbul are traversed. The number of participants in the "Maritime Transportation Satisfaction Survey" was 2,343. During this period, a model was built using the survey item "frequency of use" (dependent variable). Numerous survey examines and evaluation methodologies were utilized to determine the effectiveness of this strategy. The study examines the intermodal travel motivations and the evaluation of transportation options by multimodal users. For a successful urban transportation system, urban planning must take into account multimodal travel behavior and user expectations. There are initiatives to improve water transportation in Istanbul. Conventional maritime transportation is inadequate from start to finish. An integrated route optimization method is needed to increase the efficiency of maritime transportation. We believe that by strengthening maritime transportation links will increase water consumption. Before the coronavirus pandemic, 2,343 maritime carriers were evaluated on March 8, 2020. (Different surveys were conducted among the passengers of City Lines, Private Motors, Metrobus, and Marmaray to compare their choices and reasons.) SPSS will be used for data analysis. Multivariate Statistical Analysis relies on Multinomial Logistic Regression and Discriminant Analysis models, both of which use the K-fold and Leave-one-out criteria to decide which attributes are valid in the regression model and which are valid in the discriminant approach. The Hosmer-Lemeshow test criteria yielded a p-value greater than 0.05 for MLR characteristics.

Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach

Exploring passengers’ choice of transfer city in air-to-rail intermodal travel using an interpretable ensemble machine learning approach

Agent-Based Approach for (Peri-)Urban Inter-Modality Policies: Application to Real Data from the Lille Metropolis

Transportation authorities have adopted more and more incentive measures (fare-free public transport, construction of park-and-ride facilities, etc.) to reduce the use of private cars by combining them with public transit. However, such measures remain difficult to assess with traditional transport models. This article proposes a different approach: an agent-oriented model. To reproduce realistic applications in an urban context (a metropolis), we investigate the preferences and choices of different agents based on utilities and focus on a modal choice performed through a multinomial logit model. Moreover, we propose some methodological elements to identify the individuals' profiles using public data (census and travel surveys). We also show that this model, applied in a real case study (Lille, France), is able to reproduce travel behaviors when combining private cars and public transport. Moreover, we focus on the role played by park-and-ride facilities in this context. Thus, the simulation framework makes it possible to better understand individuals' intermodal travel behavior and assess its development policies.

Modeling the capacity of multimodal and intermodal urban transportation networks that incorporate emerging travel modes

Given the increasing prevalence of new urban transport modes such as ridesharing, e-hailing, and combined transport, it is essential to evaluate their effects on the capacity of transportation networks. Hence, this paper develops a novel transportation network capacity model to capture the travel behaviors of inter-multimodal mobility in an urban transportation system that incorporates emerging travel modes. The novel model is formulated as a bi-level programming problem, in which the lower-level model is a combined modal split and traffic assignment (CMSTA) problem based on mathematical programming. The CMSTA problem adopts the cross-nested logit model to account for intermodal travel behavior in the modal split phase and the path-sized logit model to account for route overlap in the traffic assignment phase. Moreover, the logit-based trip distribution model is used to capture the dispatch of the e-hailing traffic flow and the matching of ridesharing drivers with passengers. Besides, we consider flow interactions (e.g., cars and buses sharing the same link) in the road network. We customize a solution framework for solving this novel model that adopts the recently developed fast path-based algorithm with the Barzilai–Borwein stepsize strategy to efficiently solve the CMSTA problem, and derive a sensitivity analysis-based (SAB) algorithm to solve the entire bi-level programming problem. The effectiveness of the novel model is verified in numerical experiments that demonstrate the effects of intermodal transportation, e-hailing, and ridesharing on the capacity of a multimodal transportation network.

Evaluation of operational performance as a means for deciding about intermodal passenger transport improvements in urban environments

Regarding intermodal urban transport systems for passengers, there is no recognized standard for performance analysis. Detecting the weaknesses of such systems can allow managers to carry out more assertive, corrective actions and make them more attractive to users. The main objective of this article is to propose a model for evaluating the performance of intermodal travel for passengers in urban environments. The exploratory factor analysis was the method used to evaluate and classify the variables that constitute the proposed standard. The results show that the factors related to travel time, infrastructure and available information are the most important characteristics for the interviewed public. The operational performance analysis carried out in the Metropolitan Region of Recife, state of Pernambuco, Brazil, reveals the difficulties faced by the citizens while making an intermodal trip, which may be contributing to a reduction in the number of users of the passenger transport system.

Business Model Blueprints for the Shared Mobility Hub Network

Shared (electric) mobility is still facing challenges in terms of reaching its potential as a sustainable mobility solution. Low physical and digital integration with public transport, a lack of charging infrastructure, the regulatory barriers, and the public nuisance are hindering the uptake and organization of shared mobility services. This study examines the case of the shared mobility hub, a location where shared mobility is concentrated, as a solution to overcome these challenges. To find ideas informing how a network of shared mobility hubs can contribute to sustainable urban mobility and to overcome the aforementioned challenges, a business model innovation approach was adopted. Focus groups, consisting of public and private stakeholders, collaboratively designed five business model (BM) blueprints, reaching a consensus about the value creation, delivery, and capture mechanisms of the network. The blueprints, defined as first-/last-mile, clustered, point-of-interest (POI), hybrid, and closed mobility hub networks, provide alternative solutions to integrate sustainable transportation modes into a coherent network, enabling multi- and intermodal travel behaviour, and supporting interoperability, sustainable land use, and ensured access to shared (electric) travel modes. However, which kind of network the local key stakeholders need to commit to depends on the local policy goals and regulatory context.

Improving rural accessibility by locating multimodal mobility hubs

In rural regions, public transportation is often characterized by low accessibility as well as long waiting and travel times. In order to improve rural transportation systems, public decision-makers intend to implement alternative on-demand mobility modes. Herein, new intermodal travel itineraries with transfers at multimodal mobility hubs may enable faster public connections and thereby strengthen public transportation. Against this background, we present a decision support tool for locating multimodal mobility hubs to improve intermodal accessibility. As objectives, we aim at maximizing accessibility to workplaces and to places of private need. Our model decides on locations of multimodal mobility hubs and on the available on-demand mobility modes offered in addition to existing public transportation. We develop our model in an agile process together with rural decision-makers in the district of Heinsberg, Germany, and apply it in a real-world case study. As input for our model, we account for the existing public transportation system, identify points-of-interest, and estimate commuting volumes to workplaces based on official commuting data. Results promise a high potential to improve accessibility in rural areas. However, most of the improvement stems from unimodal car sharing trips.

Potential Analysis of E-Scooters for Commuting Paths

The mobility needs of society are constantly increasing, resulting in congested urban areas. New mobility concepts such as e-scooters can help to reduce traffic. In particular, commuting paths, which generally remain within a specific distance, are short and manageable via an intermodal travel chain. In combination with public transport, commuting paths could be beneficial. To evaluate the potential of e-scooters used with commuting paths, a literature research focusing on mobility behavior and characteristics was conducted. In addition, an end-user survey was used to identify the ecological and economical potential for typical work routes. The research results indicate that both the mobility preferences of the users, e.g., acceptance of intermodal travel, and the technical specification of e-scooters, e.g., speed and range, meet the needs of commuting. The assessment of typical work routes shows that the use of e-scooters for the first and last mile, in combination with public transport, is highly beneficial. Furthermore, e-scooters have the potential to provide individual advantages in the areas of travel time and costs. From an ecological perspective, CO2-equivalent emissions may also be reduced for some users depending on the substituted modes.

Heterogeneity in passenger satisfaction with air-rail integration services: Results of a finite mixture partial least squares model

Abstract Air-rail integration services (ARIS) are a promising solution for intermodal travel, but they also offer challenges such as trip complexity and uncertainty that may negatively impact passenger satisfaction. This paper addresses the quality evaluation of ARIS with a twofold purpose, specifically, (1) to explore the causal relationships between the psychological and behavioral variables of passengers and (2) to identify different passenger groups for precise service improvements. The relationships among passenger expectations, perceived quality, perceived value, satisfaction, complaints and loyalty are hypothesized and analyzed by using a passenger satisfaction index (PSI) model based on a representative sample of 1,345 intermodal passengers at Shijiazhuang Zhengding International Airport (SJW). Next, a finite mixture partial least squares (FIMIX-PLS) technique is applied to explore the heterogeneity. The results suggest that perceived quality, perceived value and passenger expectations have a positive effect on passenger satisfaction, which, in turn, influences passenger loyalty and complaints. Moreover, three passenger segments are found, namely, “perceived value and expectation-oriented passengers”, “perceived quality-oriented passengers”, and “satisfied loyal-conscious passengers”. Interestingly, an overall similarity in the perceived quality among the three passenger segments is observed with the importance-performance map analysis (IPMA) method. Nevertheless, some noticeable differences in the psychological-behavioral relationships of the three passenger segments are identified. Finally, managerial implications are discussed.

Modeling intermodal travel behavior in an agent-based travel demand model

The topic of intermodal passenger mobility has become more important during the last 20 years. As mobility options increase in number and flexibility, it gets more and more attractive to combine multiple modes on single trips. In addition, intermodal travel behavior is expected to contribute to less car dependent mobility and transport sector’s reduction of greenhouse gas emissions. Creating and improving the conditions for such a behavior requires planning with knowledge about influencing factors and highest resistances. Empirical evidence and behavioral models can support decisions on measures improving intermodal travel supply. This work presents an agent-based model approach containing intermodal travel behavior with regard to its most important decisions. It enables the combination of a multitude of modes and can be extended to even more modes. By combining many decisions and influences it is comprehensible and adaptable to different surveys and circumstances. We show that results are realistic and impacts are valid to be able to forecast effects of potential measures.

Travel time analysis in the Chinese coupled aviation and high-speed rail network

Abstract Thanks to the rapid expansion of the Chinese Aviation (A) network and of the High-Speed Rail (HSR) network, intermodal travel across air transport and the high-speed rail network has become a fully integrated process for many inter-city travelers. By constructing the spatially-embedded Coupled Aviation and High-Speed Rail (CAHSR) network, whose two layers respectively represent the aviation and the HSR network, while the coupling describes ground transfer between different facilities (airports and/or rail stations) in the same city, we focus on a systematic travel time analysis for major mega-regions. Our empirical analysis calculates passengers’ end-to-end travel time between major mega-regions, including real information on waiting and transfer time. The results indicate that sufficient frequencies of flights/HSR trains have led to high multi-modal accessibility across different time periods of the day. In addition, we also find that highly intermodal air-HSR mobility pathways can be extremely important to link small cities to urban mega-region hubs. Our findings may assist timetable improvement in future infrastructure planning for the CAHSR network.

Developing a user typology considering unimodal and intermodal mobility behavior: a cluster analysis approach using survey data

This paper aims to develop a user typology which enables user-specific analyses in respect of mobility behavior. It addresses the challenge of integrating unimodal and intermodal travel behavior into a user typology to obtain an overview of intermodal users within the context of their overall mobility behavior. The user typology is based on two cluster analyses (agglomerative hierarchical clustering) which use quantitative survey data on unimodal and intermodal mobility behavior obtained for Berlin, Germany. One cluster analysis was performed for unimodal use and one for intermodal mode use to take into account the users’ relatively low use of intermodal modes as well. The analyses resulted in 6 intermodal and 5 unimodal clusters based on users’ mobility behavior. Since in each case every individual is assigned to one intermodal and one unimodal cluster, the resulting intermodal and unimodal clusters were then combined in order to represent the overall mobility behavior of each individual as mobility types. The mobility types are further characterized by information on socio-demographics and mobility resources obtained from the dataset. These enhanced mobility types (EMT) provide a clearer impression of the users’ characteristics and needs. This user typology takes account of the wide range of mobility options available in cities today and the resulting diversity in people’s mobility behavior. To enable us to address the needs of users who combine several modes of transport within one trip, the proposed procedure approaches the challenge of integrating intermodal behavior into user types. The results provide a user typology which combines intermodal and unimodal travel behavior with personal characteristics and enable researchers and practitioners to work on user-specific research questions and planning tasks.

Intermodal mobility in cities and its contribution to accessibility

ABSTRACTCities offer a wide range of different mobility options, including a dense public transport network and good conditions for walking and cycling. They provide good basic requirements for using and combining different modes of transport in people’s everyday mobility in a flexible, individual and situational way. In larger cities in particular, intermodal mobility plays a significant role and is being discussed as crucial to minimizing traffic congestion, emissions and the demand for parking space.Although a common practice, intermodal mobility has so far received little attention in empirical and theoretic mobility research. This contribution addresses this gap. It compares unimodal and intermodal travel modes, and discusses their effects on accessibility in cities. Using the city of Berlin, Germany, as example, it explores intermodal mobility practice and the extent to which this relates to accessibility in cities compared to unimodal modes. Based on empirical survey data and calculations of spatial accessibility indicators, we present the performance of unimodal and intermodal travel modes using accessible locations and distance over time, and set them against the frequency of using the various modes of unimodal and intermodal travel.The combination of empirical and modeling data provides new insights into how accessibility measures based on structural data fit together from a user’s perspective and can complement each other. Based on this, we discuss further aspects of accessibility relevant for intermodality.

Modelling intermodal travel in Switzerland: A recursive logit approach

We use data from the Swiss national household travel survey to 1. analyze the socioeconomic determinants for intermodal travel in Switzerland and 2. estimate a first large-scale multimodal recursive logit route choice model for urban trip making. We show that intermodal travel is mostly associated with ownership of transit subscriptions, which allow free at the point-of-use public transportation. We also present a framework using open-source data to generate the multimodal network for the recursive logit model estimation. The fact that the model only needs a multimodal network to model the transport supply makes it independent of path sampling algorithms for the choice-set construction and it thus provides an alternative to classic mode and route choice models, since it can estimate mode and route choice parameters with directly observed routes, removing the sampling bias. By eliminating the need to sample alternative paths for estimation, it also simplifies the estimation process, making it a viable choice as an integral solution for joint route and mode choice modelling.

Combining means of transport as a users' strategy to optimize traveling in an urban context: empirical results on intermodal travel behavior from a survey in Berlin

Intermodal travel behavior is becoming increasingly important, particularly in large cities. Using and combining different transport modes flexibly on a single trip is discussed as being crucial to a more efficient and sustainable urban transport system. However, research on intermodality has mainly addressed long-distance traffic or specific combinations. This study analyzes how intermodality is practiced in everyday mobility by looking at relevant mode combinations, trip purposes, spatial differentiation and the requirements of intermodal users. The article presents results on intermodal travel behavior from a survey conducted in different neighborhoods in Berlin in 2016 (n = 1098). The results show that many people use intermodal combinations in their everyday mobility. Intermodal travel behavior is significantly characterized by public transport modes and trips to work. Spatial differences in intermodal use become obvious with combinations of bike and public transport being strong in urban neighborhoods, car and public transport in decentralized neighborhoods and combinations of different means of public transport in well-connected neighborhoods. In addition, the study emphasizes that time efficiency is an important aspect for intermodal users, becoming apparent in the reasons they state for performing intermodal trips and their evaluation of interchanges. Intermodal travel behavior and users' needs must therefore be taken into account in urban planning if the aim is to make the most of intermodal combinations for an effective urban transport system.

Intermodal, urban, mobil – Charakterisierung intermodaler Wege und Nutzer am Beispiel Berlin

Intermodal travel behaviour – the combination of different modes on a single trip – is increasingly taken into consideration in everyday mobility. In particular, urban areas face negative consequences from high car traffic and scarce parking space. Simultaneously, larger cities often have a well-developed public transport system and good accessibility by bike or on foot, which allows the use of other modes than the car. Intermodal travel behaviour enables an individual and flexible mobility. This means that persons rather use and combine different transport modes according to their specific travel needs than committing themselves to one single mode of transport. On the contrary, the use of more than one mode of transport also requires more extensive planning of the trips, the willingness to change, and specific mobility skills and information. Using the example of Berlin, this article discusses intermodal travel behaviour in the context of everyday mobility in European cities. Therefore, we analyse the amount and the specific characteristics of intermodal trips. Then, persons with intermodal travel behaviour are identified and examined in comparison to unimodal reference groups. The results show that only a small amount of trips is currently intermodal. These trips are on average longer than unimodal trips and are routine trips (e.g., trips to work) covered by two or more different modes of transport. Moreover, intermodal users differ from unimodal users: persons who combine two or more transport modes on work trips tend to be younger, female, and have a higher education level. They have diverse mobility resources available in their households, although they have to plan their usage with other household members. Furthermore, intermodal travel behaviour varies depending on the residential location within the city. The findings suggest that specific user groups and their demands must be addressed when expanding and optimizing intermodal mobility options.