Multimodal Travel Research Articles

Q_EDQ: Efficient path planning in multimodal travel scenarios based on reinforcement learning

Recently, Mobility as a Service (MaaS) has garnered increasing attention by integrating various modes of transportation to provide users with a unified travel solution. However, In multimodal transportation planning, we primarily face three challenges: Firstly, a multimodal travel network is constructed that covers multiple travel modes and is highly scalable. Secondly, the routing algorithm fully considers the dynamic and real-time nature of the multimodal travel process. Finally, a generalized travel cost objective function is constructed that considers the psychological burden of transfers on passengers in multimodal travel scenarios. In this study, we firstly constructed an integrated multimodal transport network based on graph theory, which covers four transport modes, namely, the metro, the bus, the car-sharing and the walking. Subsequently, by introducing a double-Q learning mechanism and an optimized dynamic exploration strategy, we propose a new algorithm, Q_EDQ, the algorithm aims to learn the globally optimal path as efficiently as possible, with faster convergence speed and improved stability. Experiments utilizing real bus and metro data from Xi’an, Shaanxi Province, were conducted to compare the Q_EDQ algorithm with traditional genetic algorithms. In the conducted four experiments, compared to the optimal paths planned by traditional genetic algorithms, the improved Q-algorithm achieved a minimum efficiency increase of 12.52% and a maximum of 35%. These results demonstrate the enhanced capability of the improved Q-algorithm to learn globally optimal paths in complex multimodal transportation networks. Compared to the classical Q algorithm, the algorithmic model in this study shows an average performance improvement of 10% to 30% in global optimal path search, as well as convergence performance including loss and reward values.

Optimization Method for Allocating Peak-Period Parking Demand in Hub Parking Lot Clusters

With the expansion of urban scale and the popularization of multi-modal transportation, transportation hubs, as the link of multi-modal travel, are becoming increasingly important in urban development and residents’ lives. In situations of high parking demand, the increase in road traffic volume and parking search delays exacerbates the service pressure on hub parking lots and the traffic congestion on surrounding roads. Therefore, reasonable parking demand allocation is one of the key solutions to this problem. Based on the analysis of the vehicle parking search process, this paper constructs a model for estimating parking search delay on roads outside hub parking lots and proposes an optimization model for parking demand allocation aimed at minimizing the total parking search delay of vehicles. Finally, taking a major transportation hub in Nanjing as a case study, data were obtained through field investigations and simulation experiments to identify peak parking demand periods and calibrate the model parameters. The results show that the average vehicle delay was reduced by 4.5%, with a total reduction of 13,860 s in vehicle delay for parking demands at the hub within one hour. Therefore, by optimizing the allocation of parking demand, the average delay for vehicles searching for parking can be reduced to a certain extent.

The influence of residential location and public transit options on commuters’ intention to use travel apps for different travel purposes

While living in city centers is usually linked to higher accessibility levels, shorter travel times, and higher levels of public transit (PT) utilization, the opposite is true for residents of suburban areas. This assumption holds in metropolitan contexts, where central areas offer better accessibility and are associated with higher levels of PT use. In metropolitan peripheries, a large part of commuting is done on an interurban basis, so that the level of use of public transit can be linked to the supply and information available. This work aims to understand the conditions in terms of transit supply and land use, considering the most frequent trip, psychological variables, and the modal choice of commuters’ motorized modes, in the intention to use the new real-time multi-modal travel app, such as advanced traveler information systems (ATIS) for digital mobility-management assistance. A Structural Equation Model is developed to empirically test a sample of 768 respondents collected in two suburban corridors in the Lisbon Metropolitan Area (LMA). Finally, our findings indicate that residential location, mode choice, and trip complexity have a relevant influence on the intention to adopt travel apps. Male students belonging to Generation Y/Z are the most likely users of travel apps. Regardless of the reason, travel patterns associated with more complex (more transfers) and more frequent trips can reinforce the intention to use apps. Also, it is worth noting that students are frequent public transit users, and public transit is also related to trip complexity.

Green commuting within the x-minute city: Towards a systematic evaluation of its feasibility

In cities, carbon emissions associated with commuting transport is large and significant. This study integrates data about the jobs-housing relationship, road network configurations, public transport availability, and real-time traffic conditions during peak hours to evaluate the commuting feasibility and performance of green travel modes (walking, cycling and public transport) and explore the potential determining factors. In the context of the x-minute city, the green travel commuting feasibility (GTCF) indicator measures the percentage of the working population who can commute via green travel modes within specific x-minute thresholds. 15, 30, 45 and 60 min have been considered. In comparison, the car travel commuting feasibility (CTCF) indicator is developed to evaluate the corresponding commuting performance by car. Nanshan district of Shenzhen in China is taken as a case study. Results show that distinct gaps exist between GTCF and CTCF. Public transport performs well only for long-distance (> 8 km) commuting trips, and cycling does well for short (< 3 km) and medium-distance trips. Geographically, areas with large differences of GTCF and CTCF are identified for improving green travel modes with priority. Potential factors influencing GTCF are explored with a regression model and case-based analysis. Smaller street blocks, bus route realignment and better jobs-housing balance should be targeted. Designing cycling short-cuts and public transport routes that avoid traffic jams are also recommended to promote green commuting. The findings demonstrate that real-time trip-planning information is of great value in evaluating the commuting feasibility of multimodal travel and identifying influential factors for achieving the x-minute city.

Model of Sustainable Household Mobility in Multi-Modal Transportation Networks

Nowadays, urban and suburban areas face increasing environmental pressures, and encouraging sustainable transportation behaviors at the household level has become crucial. This paper presents a model of a decision support system (DSS) for promoting sustainable household mobility choices in multi-modal transport networks. The system was modeled using an enhanced Petri Net approach, allowing for the dynamic representation of complex transport networks and multi-modal journey options. The model incorporated various sustainability factors. These were combined into a single environmental impact score, which was considered alongside travel time and cost in the route optimization process. Simulation results demonstrated the DSS’s capability to guide users toward more sustainable mobility choices. The model also showed potential as a tool for policymakers to assess the impact of various sustainable transportation initiatives and infrastructure investments. This paper discussed the versatile applications of the system. It also addressed the limitations of Petri Net models in transportation systems and suggested future research directions.

Road–Rail Intermodal Travel Mode Choice Behavior Considering Attitude Factors

Road–rail intermodal transportation (RRIT) leverages the advantages of multiple transport modes and is crucial for addressing the current issue of imbalanced development in the transportation sector. However, passengers’ behavior in choosing RRIT remains unclear, and it is necessary to optimize travel service quality through analyzing RRIT choice behavior based on user perceptions. This study designed a stated preference experiment that included both direct and multi-modal travel options. A hybrid choice model considering attitude variables was constructed, and four latent attitude variables—convenience, economy, comfort, and riskiness—were extracted to analyze their impact on intercity travel mode choice behavior under conditions of ticket booking uncertainty. The results revealed that the ticket booking success rate is a critical factor in travelers’ decision-making. Passengers tend to choose travel options with higher ticket booking success rates, even if it entails a slight increase in the ticket prices for the high-speed rail to high-speed rail transfer option. The attitude variables significantly influence intercity travel mode choice behavior, with travelers generally exhibiting a preference for risk avoidance in their travel options. Moreover, there are differences among various groups of travelers in their preferences and demands for the convenience, economy, and comfort aspects of travel options. These research findings can enhance our understanding of the key factors influencing the selection of RRIT services, thereby supporting RRIT designers and planners in improving service quality and facilitating the future growth of RRIT.

Dynamic learning-based search for multi-criteria itinerary planning

Travelers expect integrated and multimodal itinerary planning while addressing their individual expectations. Besides common preferences such as travel time and price, further criteria such as walking and waiting times are of importance as well. The competing features of these preferences yield a variety of non-dominated itineraries. Finding the set of non-dominated multimodal travel itineraries in efficient run time remains a challenge in case multiple traveler preferences are considered.In this work, we present a sampling framework to approximate the set of non-dominated travel itineraries that scales well in terms of considered preferences. In particular, we guide the search process dynamically to uncertain areas of the complex multimodal solution space. To this end, we learn the structure of the Pareto front during the search with Gaussian Process Regression (GPR). The GPR sampling framework is evaluated integrating an extensive amount of real-world data on mobility services. We analyze long-distance trips between major cities in Germany. Furthermore, we take up to five traveler preferences into account. We observe that the framework performs well, revealing the origin and destination specifics of Pareto fronts of multimodal travel itineraries.

Exploring the role of Mobility-as-a-Service in morning commuting trips

Promising the seamless integration of multiple transportation modes, Mobility-as-a-Service (MaaS) has gained popularity over the years, yet its effectiveness in enticing private car users and improving travel efficiency remains uncertain. This study explores the competitiveness of MaaS-enabled multi-modal travel options versus private car usage through equilibrium analysis. In addition to pricing that affects the fixed cost for travel, we examine the often overlooked inconvenience cost associated with multi-modal trips. We establish our analysis for the commuting problem of a one-origin-one-destination network, where a highway and a mass transit line connect the residential area and the central business district (CBD) area. Travelers choose their departure time and travel mode among auto, Park-and-Ride (PnR), ride-hailing and transit (RnT) to minimize their total travel cost. Inconvenience costs associated with searching for parking and waiting for ride-hailing are explicitly modeled. We analytically provide the mode share and departure time windows under all possible equilibria. Our findings reveal the complex nature of mode choice, distinctly affected by fixed and inconvenience costs, with demand playing an even more significant role. Notably, fixed costs set an entry fee to adopt different modes, and the feature of inconvenience costs affects the utilization of available transportation resources. More importantly, to benefit the overall system, we encourage maintaining a balanced mode share by implementing pricing and capacity strategies rather than aiming for a completer transition of private car users to MaaS.

Mobility as a Service: An exploration of exact and heuristic algorithms for a new multi-modal multi-objective journey planning problem

Mobility as a Service (MaaS) is a term coined for the development and implementation of multi-modal trip planner recommendation systems. Multi-modal journeys can include public transport, private transport and hire-able e-scooters which can beneficially augment public transport journeys. This study proposes and tackles a new and more general multi-modal multi-objective journey planning problem than considered previously. Our aim is to generate Pareto sets of multi-modal journeys which minimise the objectives of cost, travel time, inconvenience, CO2 emissions, and calorie expenditure, as well as find journeys that optimally balance the trade-offs between them. Commuters can then choose between green, cheap, fast, car-free or convenient journeys. This work proposes implicit enumeration and heuristic algorithms which are analysed in terms of their theoretical time complexity, empirically in terms of solution time and optimality gap using a series of Manhattan and random structure transport networks. We show that our algorithms can generate solutions of equal quality to RAPTOR—a prominent existing journey planning algorithm. For this problem we reveal that the number of network nodes is a huge computational bottleneck, leading to the suggestion that future research can benefit from limiting the number of network nodes that are considered as transfer points. While our heuristic can generate good quality Pareto sets quicker than the enumeration procedure for the largest instances considered, we show that the enumeration procedure with pruning rules can still be the most effective strategy for this particular problem.

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

Mining Multimodal Travel Mobilities with Big Ridership Data: Comparative Analysis of Subways and Taxis

Understanding traveler mobility in cities is significant for urban planning and traffic management. However, most traditional studies have focused on travel mobility in a single traffic mode. Only limited studies have focused on the travel mobility associated with multimodal transportation. Subways are considered a green travel mode with large capacity, while taxis are an energy-consuming travel mode that provides a personalized service. Exploring the relationship between subway mobility and taxi mobility is conducive to building a sustainable multimodal transportation system, such as one with mobility as a service (MaaS). In this study, we propose a framework for comparatively analyzing the travel mobilities associated with subways and taxis. Firstly, we divided taxi trips into three groups: competitive, cooperative, and complementary. Voronoi diagrams based on subway stations were introduced to divide regions. An entropy index was adopted to measure the mix of taxi trips. Secondly, subway and taxi trip networks were constructed based on the divided regions. The framework was tested based on the automatic fare collection (AFC) data and global positioning system (GPS) data of a subway in Beijing, China. The results showed that the proportions of taxi competition, taxi cooperation, and taxi complements were 9.1%, 35.6%, and 55.3%, respectively. The entropy was large in the central city and small in the suburbs. Moreover, it was found that the subway trip network was connected more closely than the taxi network. However, the unbalanced condition of taxis is more serious than that of the subway.

Heterogeneous choice of personalized Mobility-as-a-Service bundles and its impact on sustainable transportation

The Mobility-as-a-service (MaaS) framework has the potential to induce changes in individual travel behavior towards more sustainable transportation alternatives by integrating multimodal travel services. However, insights into the behavioral responses of specific groups of people to MaaS, thus promoting green travel, are currently lacking. This paper intends to investigate the intergroup heterogeneous preferences on the choice of MaaS bundles and evaluate their contributions to eco-friendly travel. A stated choice experiment is designed, incorporating personalized MaaS service bundles complemented by different green travel-oriented ticketing strategies. The experiment caters to different groups of users related to public transport, cars, and other modes of transport. The results indicate a substantial improvement in the goodness-of-fit of the behavioral model after user classification, revealing significant heterogeneity in preferences among users regarding MaaS bundle choices. Notably, the impact of sub-tickets on nudging toward green transportation is more pronounced than that of monthly and discount tickets.

Geographic Information System-Based Comprehensive Shared Micromobility Station Siting Optimization for Small Urban Areas

Shared micromobility travel modes such as dockless e-scooters and bikeshare programs have become increasingly popular in the U.S. in the last decade because of their potential to improve multi-modal accessibility within communities. Smaller urbanized areas with lower population densities and fewer resources for system planning, operation, and maintenance present unique challenges with siting optimal station/service area locations. This research develops a comprehensive geographic information system (GIS)-based methodology for optimizing micromobility stations/service area locations using available and rasterized geospatial data to capture bikeshare demand indicators. Inputs are prioritized by overall importance according to the results of a survey of transportation professionals, with weights calculated by an analytic hierarchy process. These different factors are combined to create a new spatial index value to identify hotspots for candidate station/service area locations, which can be further analyzed to choose optimal locations based on the budgeted quantity of station/service area locations and ideal spacing between stations/service areas. The case study of the methodology is presented on a bikeshare station siting study in Iowa City, Iowa, U.S., using data from the Metropolitan Planning Organization of Johnson County. This research seeks to improve shared micromobility station/service area planning to better orient service to a variety of transportation goals including regular/commuting use, recreational use, equity, first/last mile connection to transit, and operational partnership opportunities. Multimodal travel times and job accessibility in the study area are evaluated both before and after the introduction of bikeshare, and both greatly improve with the introduction of optimal stations. Public agencies could expect to benefit from this comprehensive methodology because it uses easily obtainable data sources and provides the flexibility to weight the importance of factors differently to fit their communities’ specific transportation goals.

Machine Learning Driven Smart Transportation Sharing

In many urban areas, traffic congestion has become one of the most challenging issues of modern life, resulting in detrimental effects on the environment, productivity loss, fuel wastage, and longer travel times. As a solution, people are increasingly turning to shared transportation modes due to the convenience of multi-modal journeys facilitated by smart transportation systems. The last mile problem refers to the fact that, in large cities, buses and trains deliver passengers to transit stations close to retail and job areas, leaving them needing another form of transportation to reach their final destination. By promoting the use of public transportation and addressing this issue, a smart bike-sharing system can contribute to reducing traffic congestion. The study presents a review of various methods that are associated with the designing of the bike sharing system and suggests a model incorporating various methods to derive solutions, with a focus on utilizing clustering algorithms for the analysis of the provided time series dataset. The study reveals that the application of algorithms such as the K-Means algorithm, Fuzzy C-means, etc. would be very effective in visualizing the resulting clusters and improve the forecasting accuracy.

Efficiency and equality of the multimodal travel between public transit and bike-sharing accounting for multiscale

As a supplement to the existing public transit system, bike-sharing is considered an effective solution to the “first mile” and “last mile” of travel. While many stakeholders believe that multimodal travel between public transit and bike-sharing can improve urban accessibility and sustainability, few studies have assessed the impact of bike-sharing on existing public transportation systems in terms of efficiency and equality. This research uses three months of mobile phone location data and about 140 million bike-sharing trips (origin–destination, OD) data from Shenzhen, China, to analyze first mile and last mile bike-sharing multimodal travel and measure the impact of bike-sharing on the existing public transportation system in terms of efficiency and equality at different scales. The research finds that bike-sharing is less effective in improving the operational efficiency of urban public transport and creates new inequalities at both global and local scales of the urban public transport system. Bike-sharing is only effective in tiny areas of the city and specific modes (subway-bike-sharing) and does not benefit groups with low socioeconomic levels and those living in edge areas of the city. Improving the equity and accessibility of public transportation is a key factor towards promoting sustainable urban development, and the analysis of this study on multimodal travel efficiency and inequality of bike-sharing can provide helpful insights for future sustainable urban planning.

Prioritizing a research agenda on built environments and physical activity: a twin panel Delphi consensus process with researchers and knowledge users

BackgroundThe growth of urban dwelling populations globally has led to rapid increases of research and policy initiatives addressing associations between the built environment and physical activity (PA). Given this rapid proliferation, it is important to identify priority areas and research questions for moving the field forward. The objective of this study was to identify and compare research priorities on the built environment and PA among researchers and knowledge users (e.g., policy makers, practitioners).MethodsBetween September 2022 and April 2023, a three-round, modified Delphi survey was conducted among two independent panels of international researchers (n = 38) and knowledge users (n = 23) to identify similarities and differences in perceived research priorities on the built environment and PA and generate twin ‘top 10’ lists of the most important research needs.ResultsFrom a broad range of self-identified issues, both panels ranked in common the most pressing research priorities including stronger study designs such as natural experiments, research that examines inequalities and inequities, establishing the cost effectiveness of interventions, safety and injuries related to engagement in active transportation (AT), and considerations for climate change and climate adaptation. Additional priorities identified by researchers included: implementation science, research that incorporates Indigenous perspectives, land-use policies, built environments that support active aging, and participatory research. Additional priorities identified by knowledge users included: built environments and PA among people living with disabilities and a need for national data on trip chaining, multi-modal travel, and non-work or school-related AT.ConclusionsFive common research priorities between the two groups emerged, including (1) to better understand causality, (2) interactions with the natural environment, (3) economic evaluations, (4) social disparities, and (5) preventable AT-related injuries. The findings may help set directions for future research, interdisciplinary and intersectoral collaborations, and funding opportunities.

Traveling itinerary problem in a scheduled multimodal transportation network for a fixed sequence of cities.

Developing an efficient and economical journey plan in multimodal transportation networks is of significant and fast-growing importance, but it is still an annoying experience for a traveler. This paper aims to find the journey plan at a combined cross-border and inter-regional level when visiting a sequence of cities while utilizing several transport modes to reduce travel costs and planning time. We study a traveling itinerary problem in a scheduled multimodal transportation network with constraints on both arcs and nodes as a new extension of the shortest path problem. We formulate a 0-1 integer linear programming model for the traveling itinerary problem and develop an exact algorithm that finds a combined cross-border and inter-regional low costs journey plan. We present case studies based on real-world transport data to illustrate the usefulness and computational efficiency of the proposed approaches. We compare the results with the previously proposed approach to demonstrate the benefits of multimodal journeys. Finally, we compare the results with the solution obtained by the general-purpose 0-1 integer linear programming solver to evaluate the computational time.

Multimodal experience as a predictor and catalyst of travel behavior

Three surveys and an intervention experiment among university students investigated the relationship between past experience with multimodal transportation and current travel behavior. The case study university primarily draws students from rural, car-oriented communities but also attracts students from international and domestic urban cities where walking, biking, and public transit are common. Nearly 800 students responded to the first survey. They confirmed the hypothesis that rural students lack experience with multimodal travel and are less likely than their urban classmates to use multimodal opportunities in their new environment. Next, a subset of students was surveyed before and after participating in a multimodal exposure tour (the treatment group). Their responses were compared to students who completed the same surveys but did not participate in the tour. The results indicate that the treatment group increased their use of bus, bike, and walking. The exposure experiment should be interpreted with caution due to a small sample size, nevertheless, the results showed statistical and practical significance and were supported by the initial survey that involved a large, reliable sample. This case study is a microcosm of people moving to new mobility environments and the potential to transform travel habits. The survey design and tour event provide a model for investigating previous travel experience and promoting change in mode choice. The approach of this study can help planners, policymakers, business leaders, and university administrators better understand and shape the travel behavior of their constituents.

Gravity-based models for evaluating urban park accessibility: Why does localized selection of attractiveness factors and travel modes matter?

Gravity-based models have been extensively utilized in urban studies for measuring geographic disparities in access to urban parks over the past several decades. However, despite methodological advancements incorporating various aspects of accessibility, there has been limited focus on the impact of variable selection (e.g., attractiveness factors) and transport modes on accessibility evaluations. This study investigates the differences in gravity-based models for assessing park accessibility based on varying assumptions about attractiveness factors and travel impedance. Semi-structured interviews with local residents were conducted to identify the reasons for park visits in Shanghai. Our bivariate correlation analyses reveal that factors such as park openness and access to public transport were crucial, in addition to conventional factors identified in the literature (i.e., park size and driving accessibility). This insight led to the development of localized accessibility measurements that incorporate park inclusiveness (i.e., entrance fees and opening hours) and multimodal travel options (based on multinomial logistic mode choice models). The results indicate that the refined model produces lower and more varied accessibility levels, which can better capture accessibility gaps across different geographic contexts. This accurate and practical identification of accessibility gaps can assist local planners and decision-makers in formulating effective policies and strategies to promote equitable access to urban public parks.