Intermodal Mobility Research Articles

Simulating Traffic Networks

For driving the roads of cities into enjoyable and relaxing places with parks, trees, and seating, a paradigm change in everyone’s commuter behavior is needed. Still, individual transport via cars increases, and thus, the space required for parking and driving these cars shapes our cities — not the people. Next to the space needed, vehicles pollute the environment with CO2, diesel particulate, and even electric cars with tire abrasion. Alternative modes of locomotion, like public transportation and shared mobility, are still not attractive to many people. Intelligent intermodal mobility networks can help address these challenges, allowing for efficient use between various transportation modalities. These mobility networks require good databases and simulation combined into digital twins. This paper presents how such a digital twin can be created in the Simulation of Urban Mobility (SUMO) software using data from available and future city sensors. The digital twin aims to simulate, analyze, and evaluate the different behaviors and interactions between traffic participants when changing commuting incentives. Using the city of Osnabrück and its different available sensor types, the data availability is compared with other towns to discuss how the data density can be improved. Creating a static network from open street data and intersection side maps provided by the city of Osnabrück shows how these data can be integrated into SUMO for generating traffic flows and routes in SUMO based on a database of historical and live data. Within the conclusion, the paper discusses how developing a digital twin in SUMO from static and dynamic data can be improved in the future and what common misconceptions need to be overcome.

Understanding information needs for seamless intermodal transportation: Evidence from Germany

Cities worldwide are seeking to enhance their sustainable mobility by reducing individual motorized transportation. While intermodal mobility – combining multiple transportation modes in one journey – is a key solution, individuals encounter challenges initiating intermodal journeys owing to the proliferation of mobility services. Providing accurate information at the right time is crucial amidst this complexity. While research has examined information needs for each mobility mode independently, the relationships between modes, phases, and information needs have barely been empirically investigated. Through a sequential mixed-method approach involving a literature review and a survey of >500 participants, this study identifies and validates the concept of phase- and mode chain-sensitive information needs. The findings provide initial insights, emphasizing phase relationships, mode chain relationships, and the interplays between phases and mode chains — a holistic understanding. This research can guide the design of more effective traveler information systems, aiding the shift toward sustainable urban mobility.

Roles of accessibility and air-rail intermodality in shaping mobility patterns in mega-city regions: Behavioural insights from China

This paper aims to understand mobility patterns in mega-city regions from perspectives of accessibility and air-rail intermodality. Travellers' preferences for intercity mode and its access and egress modes were simultaneously collected in the context of the Beijing-Tianjin-Hebei mega-city region. The accessibility of transport hubs and destinations was measured to enable interpreting mobility behaviour in three dimensions, i.e., travel mode decisions, departure transport hub decisions, and transport service decisions. The calibrated behavioural model highlights a series of determinants regarding level-of-service attributes, travel characteristics, accessibility, socio-demographics, and their interaction. Based on the behavioural insights, twenty domestic cities were selected as destinations for analysing the dynamics of mobility patterns in terms of the competition within the multi-airport system and air-rail cooperation potential in the long-haul market. The elasticity of shifts in transport demand between the primary and secondary airports in the Beijing multi-airport system was assessed under various pricing strategies, highlighting the vital role of the most accessible airport in air traveller decision-making. Moreover, the results underline the effect of air-rail intermodal service upgrades, e.g., offering baggage handling systems and flexible tickets, on stimulating air-rail intermodal mobility in the mega-city region.

Beyond a transport node? What residents want from transforming railway stations

ABSTRACT Transport planning authorities and operators in various countries are planning far-reaching transformations of secondary railway stations into so-called mobility hubs with the aim to foster rail-based and intermodal mobility. The transformations of railway stations do not only affect the transportation network, but also the urban fabric surrounding railway stations. Projects of this nature, therefore, require a deeper understanding of the preferences of local residents, the key users of such hubs. We developed a three-stage adaptive survey experiment with a random sample of 2,028 adult residents in the perimeter of three secondary railway stations in Switzerland to identify preferences towards four potential railway station functions: public transport provision, access to multimodality, retail and services, and stations as a public space. The main findings are that the transportation function is the top priority, followed by the public space function. Other functions, such as retail, services, and multimodal mobility, are less preferred by residents. The findings reveal the importance of also considering non-commercial functions of these hubs and that residents are viewing railway station as more than merely transport hubs or shopping centres; they are considered key neighbourhood components that shape local identities through their role as public spaces.

Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions

The design of future mobility solutions and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. This requires tools to study such a coupling and co-design mobility systems in terms of different objectives. We present a framework to address such co-design problems, leveraging a mathematical theory of co-design to frame and solve the problem of designing and deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions and public transit, in terms of fleets sizing, vehicle characteristics, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design as the interconnection of simple components and to tackle it from a systemic perspective. Moreover, it requires general monotonicity assumptions and naturally handles multiple objectives, delivering rational, actionable solutions for policy makers. We showcase our methodology in a case study of Washington D.C., USA. Our work suggests the possibility to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and to inform policy-making in the future.

How smart is mobility in smart cities? An analysis of citizens' value perceptions through ICT applications

Urban mobility plays a key role in any modern society. The advent of digital platforms and dedicated mobile apps, as well as new business models based on the concept of access over ownership and sharing economy (sharing mobility and Mobility as a Service), promises to reshape this sector. It will facilitate and speed up the diffusion of more sustainable and efficient means of transportation based on an intermodal mobility management perspective. The main goal of this paper is to examine the current state of smart mobility together with those factors that may influence the extent of modern sharing mobility proliferation and penetration through ICT applications and social media. The authors applied the content analysis using a sample of more than 1000 citizens' and visitors' reviews published on the official digital platforms of the smart city of Milan. The paper includes a comparison with data drawn from the public urban mobility services users. Based on the nodes resulting from the content analysis, the STEEP method was adopted in order to apply a more complex perspective concerning social, technological, environmental, economic and political issues. This strategic analytical tool was combined with the SWOT analysis dimensions to provide a comprehensive practical contribution to modern municipal smart mobility management systems. The results highlight the need for both smart mobility solutions suppliers and institutions to properly understand and communicate how digital services can be implemented across different users' touchpoints and communication channels to increase the value-added provided to citizens and visitors alike. • Urban mobility services assessment in a smart city context • Analyses of digital platforms and mobile apps for sharing mobility services • Content analysis and Network analysis of the reviews of mobility services' users • Multilevel approach based on the integration of STEEP and SWOT analyses • Theoretical contribution and strategic insights for policy-makers

GIS analysis of the Andalusian rail trails accessibility from motorhome areas

The symbiosis between motorhome tourism and cycle tourism on the rail trails is not reflected in the development strategies to promote these old railway infrastructures. The project “New models of intermodal mobility and tourist use of Andalusian rail trails: application of an explanatory, predictive and spatial methodology”, uses GIS tools and web-scrapping as an essential resource to obtain results. This paper shows results from the use of these two techniques to highlight a relationship that is ignored by the planners of the promotion strategies that have been implemented in the last twenty years about the rail trails in Andalusia. The results, based on the analysis of the primary information prepared by the authors, demonstrate that the symbiosis has occurred spontaneously without any prior planning. This paper presents also a necessary conceptualization to identify the elements that participate in this symbiosis and to develop the GIS model for further analysis. The project is financed by the Department of Economy Knowledge, Business and Universities of the Government of Andalusia.

Ride-hail to ride rail: Learning to balance supply and demand in ride-hailing services with intermodal mobility options

Growing spatiotemporal imbalance in supply and demand in ride-hailing services has been arousing concern. A variety of measures in the perspective of ride-hailing services per se is studied. However, approaching the imbalance problem in the broader perspective of intermodal mobility, which integrates different modes of passenger transportation in a single trip and aims to overcome limitations of any unimodal mobility, remains to be explored. This paper aims to investigate the potential of introducing intermodal mobility options to balance ride-hailing services. We first identify the importance of the availability decision on intermodal mobility options. Then we formulate the availability decision problem as a Markov decision process (MDP). Due to its convoluted system dynamics and large state space, we cast the intractable MDP into a reinforcement learning (RL) problem to approximately learn the availability policy. To stabilize the learning processes, we model the intermodal ride-hailing services as a stochastic queueing network and tailor a family of state-of-the-art RL algorithms to iteratively evaluate and improve the availability policy. Lastly, we test this optimization framework in a large-scale intermodal mobility scenario calibrated with real-world trip data. Results show that the learned availability policy can significantly dissipate riders’ queue and improve the service rates towards more balanced supply and demand.

Nonlinear effects of the built environment on metro-integrated ridesourcing usage

Although ridesourcing has served as an emerging feeder mode to the metro system to solve the first/last-mile issue, research on metro-integrated ridesourcing usage is rather limited. This paper applies a gradient boosting decision tree (GBDT) method to investigate the nonlinear relationship between the built environment and metro-originated and metro-destinated usage, using ridesourcing trip record data. The results show that built environment factors (i.e., density, diversity, and destination accessibility) have significant nonlinear and threshold effects on the integrated usage, which differentiate between weekdays and weekends. Different patterns are also observed between metro-originated and metro-destinated usage. Employment density has a more significantly positive effect on weekday metro-originated usage than metro-destinated usage. The distance to metro stations does exist an effective range. These findings could help not only transportation network companies optimize ridesourcing services but also transportation planners formulate tailor-made land use interventions to facilitate intermodal mobility.

The establishment of an intermodal walkability index for use in car oriented urban environments: The case of Nicosia

Sustainable mobility has been considered key in the pursuit of sustainable development in urban environments. During the last decades, a lot of innovative initiatives have been proposed and examined, most of them were site-specific and highly relevant to the local context. This case study review focuses on Nicosia, a car oriented, medium sized city, and attempts to define an intermodal walkability index for the city center. In a city like Nicosia where more than 90% of the daily trips are done completely by car, encouraging intermodal mobility with walking as part of the trip is an important first step to sustainable mobility. The aim of the paper is to present a model based upon the most impactful sustainability indicators when referring to pedestrian mobility, and therefore will create an intermodal walkability index for grading the walkability of an urban environment, in our case a particular, car-oriented one. The index was developed by using the latest version of pyQGIS, due to the fact that the algorithms that are being used in the calculation of this index are already part of the geospatial analysis toolbox contained within QGIS. The paper uses as a case study the municipality of Nicosia in Cyprus where it was proven that pedestrian transportation can be graded in terms of walkability from the private car drop point. The study has also discovered that the use of primary electricity generation and fossil fuels for transport can be reduced if problematic areas that have a low or negative sustainability score are managed in ways that will increase the score.

Ride-Hail to Ride Rail: Learning to Balance Supply and Demand in Ride-Hailing Services with Intermodal Mobility Options

Ride-Hail to Ride Rail: Learning to Balance Supply and Demand in Ride-Hailing Services with Intermodal Mobility Options

Mining passenger’s regional intermodal mobility from smartcard data

Park & Ride (P&R) enables railway users to access the transit network by means of their own cars. Its usage at the regional level can be analyzed on the basis of Household Travel Surveys (HTS). To overcome the HTS limitations in sample size and refreshment, this paper is aimed to combine such HTS for learning the car2rail intermodality phenomenon of individual mobility, with an Automated Fare Collection (AFC) database for inferring it over a very large set of individual trips. The approach involves three steps: (i) the HTS-based featuring of Origin-Destination (O-D) trips; (ii) the treatment of the AFC dataset using the dynamic path search and ad-hoc rules based on General Transit Feed Specification (GTFS) data, to yield AFC rail O-D trips; and (iii) the supervised machine learning of P&R usage based on the HTS and AFC data, considering three methods (Support Vector Machine, Decision Tree and Artificial Neural Network). Application to the Paris – Ile-de-France region with 2010 HTS and 2019 AFC data revealed three types of intermodal trips by an unsupervised machine learning algorithm, two of them at morning peak hours with either short or long rail distances, and the last one after the evening peak.

Optimal design of intermodal mobility networks under uncertainty: Connecting micromobility with mobility-on-demand transit

Mobility-on-Demand Transit (MoDT) is a suitable solution for linking packed urban centers to low-demand suburban areas. Meanwhile, micromobility services, including dockless bikesharing and electric scooters, are growing exponentially worldwide, providing a low-cost, low-emission travel mode for short home-based trips. We propose an intermodal network in which travelers use micromobility for the first-/last-mile connections to MoDT. The optimal design of the intermodal network is formulated as a two-stage stochastic program with a revenue-maximization objective. The first stage solves the near-optimal transfer hub locations, and the second stage considers the integrated operations of the micromobility and MoDT vehicle fleet. This work contributes to the MoD literature by addressing how to coordinate the intermodal transfers and improve the utilization of vehicles with uncertain demand. The movements of these vehicles are modeled as an interconnected closed queueing network with time lags. A new starter-follower model captures the rearranged ride-pooling behavior at these selected transfer hubs. We implement this network design method to evaluate the benefit of combining a bikesharing and a MoDT network in New York City. This paper provides a systematic method for designing intermodal mobility networks, laying the foundation for multimodal mobility applications.

The Negative Effects of Institutional Logic Multiplicity on Service Platforms in Intermodal Mobility Ecosystems

Digitalization is changing the mobility sector. Companies have developed entirely new mobility services, and mobility services with pre-digital roots, such as ride-sharing and public transport, have leveraged digitalization to become more convenient to use. Nevertheless, private car use remains the dominant mode of transport in most developed countries, leading to problems such as delays due to traffic congestion, insufficient parking spaces, as well as noise and air pollution. Emerging intermodal mobility ecosystems take advantage of digital advances in mobility services by providing individual, dynamic and context-aware combinations of different mobility services to simplify door-to-door mobility and contribute to the reduction of private car use. However, the service platforms are limited in terms of functional range, for example they may lack integrated ticketing and rely on static data, which makes intermodal mobility inconvenient. This article adopts the service-dominant logic perspective to analyze service ecosystems for intermodal mobility and their service provision. Drawing on traditional institutional literature, the authors question the assumption that service logic is dominant for all actors of a service ecosystem. By applying activity theory, the article illustrates how an institutional logic multiplicity among actors can negatively affect the functional range of service platforms. The results of a qualitative study in Germany show that, in particular, the state logic of some actors, which is characterized by the obligation to provide mobility, impairs the quality of service platforms in supporting citizens in intermodal mobility.

Intermodality in European metropolises: The current state of the art, and the results of an expert survey covering Berlin, Copenhagen, Hamburg and Paris

Intermodality, defined as using more than one mode for a single trip, is frequently discussed as a measure to help achieve more sustainable mobility in Europe, especially in cities. However, intermodality presents challenges for transport providers, and has its drawbacks for users, who prefer connections that do not require changing between transport modes. As a basis for forming recommendations to facilitate intermodality, we first analysed the status quo of intermodal mobility in four European metropolises: Berlin, Copenhagen, Hamburg and Paris. Subsequently, we conducted an expert survey in which we asked experts in the four cities about the future share and development of intermodality, its future relevance and potential of intermodality in transport planning, and how various factors on both the demand and supply side influence the level of intermodal activity. According to our findings, most experts foresee an increase in the share of intermodal trips. They also believe the relevance of including intermodality considerations in transport planning to be increasing. The increase of intermodal mobility will be driven by factors such as reduced vehicle ownership, but also by new mobility patterns. The latter can be explained by social factors such as urbanisation or digitalisation that affect the lifestyle of cities’ inhabitants in future.

Intermodal mobility analysis with smart-card data. Spatio-temporal analysis of the bus-metro network of Rennes metropole

This paper aims to analyse the intermodal practices of mobility in the bus-metro network of Rennes metropole. Intermodality being strongly linked to the use of urban public transport networks, the Rennes Metropole bus and metro network data provided by Keolis-Rennes provides a very significant part of daily intermodality. To compensate for the lack of information on destinations and correspondances in the ticketing data a reconstruction of trips was made on the basis of hypotheses provided by the literature. The research carried out in the Mobilletic project considerably deepened the understanding of the temporal and spatial dimensions of intermodal mobility within Rennes Metropole’s urban public transport network.daily mobility, public transport, data science, data analysis, intermodal transportation

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 Hubs and User Needs

Technological innovation in the mobility and transport sphere is still strongly aimed at vehicle- or infrastructure-based systems. Actual user needs in regard to accessibility and usability of the links between different transportation modes are usually addressed by standardized planning processes. In a row of four consecutive Austrian research projects, every element in the chain of intermodal routes was addressed, starting from the user-centered features of public transport stops in both urban and rural transport systems, to the planning tasks involved in providing demand-driven public transport. The current iteration focuses on establishing a typology of intermodal mobility hubs, including a differentiated view of the potential users of a multimodal transport system and their respective needs at the station.

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.

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

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