In the ever-evolving world of transportation, Mobility-as-a-Service (MaaS) has emerged as a game changer, revolutionizing how we plan and navigate our journeys. A robust MaaS trip planner is at the heart of this transformation, seamlessly connecting multiple modes of transportation to offer travelers efficient, convenient, and personalized mobility solutions. In this blog, we explore the essential characteristics that define an effective MaaS trip planner and show how Metropia's innovative design principles and implementation methods have paved the way for a remarkable MaaS experience. To demonstrate the real-world impact, we will also delve into a case study of the UMAJI project in Taiwan, where Metropia's MaaS trip planner is deployed to enhance how people travel across the country. Join us as we embark on a journey into the world of MaaS, where innovation and practicality converge to shape the future of mobility.
The Needs and Challenges for a MaaS Trip Planner
In today's fast-paced world, efficient and seamless travel experiences have become a priority for both locals and tourists. Discovering and accessing sustainable transportation service options requires understanding the complexities of multimodal travel, lowering the barriers of entry and facilitating willingness to shift travel behaviors. Navigating public transportation systems in unfamiliar cities can be daunting and time-consuming. This is where a well-designed MaaS trip planner module comes in, serving as a valuable tool to simplify the process and enhance the overall travel experience.
One of the primary challenges faced by travelers is the lack of connectivity between public transportation hubs and their origin or destination points. While buses, trains, and other modes of transit provide efficient transportation within city limits, the initial and final stages of a journey, often referred to as the first-mile and last-mile, can pose difficulties. These stages involve getting to or from transit stations to the traveler's home, work, hotel, or other desired locations.
A MaaS trip planner module should provide a comprehensive solution to address these challenges. By consolidating data, including real-time data, from various transit agencies and transportation providers, it offers users a centralized platform to plan their trips effortlessly. Travelers can make informed decisions and plan their routes accordingly, to avoid unnecessary waiting time and minimize disruptions to their itineraries. This not only saves time and reduces stress but also promotes the use of public transportation as a sustainable and cost-effective travel option.
Building a trip planner to address these traveler experience needs presents several technical challenges:
Data Integration and Quality. Collecting and integrating data from multiple transit agencies, ride-sharing services, bike-sharing programs, and other transportation providers can be complex. Each data source may have its own format, APIs, and data quality standards. Ensuring data accuracy, consistency, and real-time updates across different sources is crucial for providing users with reliable and up-to-date trip information.
User Preference Multimodal Routing and Optimization. It is a challenge to design algorithms and models that can efficiently optimize routes connecting origin and destination while also considering multiple transportation modes based on unique traveler needs. The trip planner needs to calculate the most efficient and reliable routes based on factors like travel time, transfer connections, availability, and user preferences.
Real-Time Data and Updates. Integrating real-time data for transit schedules, delays, traffic conditions, and availability of ride-sharing services presents a technical challenge. The trip planner needs to continuously fetch and process real-time data to provide users with accurate and timely information. Handling data synchronization and ensuring that the trip planner reflects any changes or disruptions in real-time requires robust data management and synchronization mechanisms.
Scalability and Performance. Building a trip planner that can simultaneously handle a large number of users and provide quick responses is a challenge. The system should be scalable to handle peak usage times and capable of handling complex computations and data processing in real-time. Optimizing the performance of the trip planner to deliver fast response times, especially when dealing with multimodal routing and complex data calculations, requires efficient algorithms and infrastructure design.
Algorithms and Routing Based on Traveler Preference and Need
In the literature, there are multiple categories of algorithms, such as Dijkstra’s Algorithm, A* algorithm, Connection Scan Algorithm, Raptor Algorithm, Hierarchical Algorithm, and Time-Expanded Network, etc. It’s important to note that MaaS trip planner implementations often combine multiple algorithms and techniques to address different aspects of the planning process, such as routing, schedule adherence, real-time updates, and multimodal integration. The specific choice of algorithms depends on factors like the complexity of the transit network, real-time data availability, computational efficiency requirements, and the desired user experience.
When searching for transit routes, users often need multiple “good options” rather than one “best option.” This requirement makes traditional algorithms like Dijkstra’s or A* algorithm inapplicable. On the other hand, pre-computing routes are also computationally intractable for large networks.
A good trip planner is not only about returning the shortest or fastest options, but also about providing multiple choices that cater to the user’s needs and preferences. The design of Metropia’s MaaS Trip Planner is based on the principle of computing multiple good options in real-time, while accommodating network size and complexity.
The concept of “good options” can be subjective, which is why a reliable trip planner should also offer personalization features to accommodate different goals. Users may have specific criteria such as searching by departure time or arrival time, prioritizing faster but more expensive options, or opting for cheaper but slower alternatives. Additionally, physical characteristics like walking speed can impact the ability to catch transfers. There are instances where users may specify minimal transfer times at certain terminals to meet friends or for shopping purposes, rather than solely focusing on the fastest option.
Ensuring a good trip planning experience involves striking a balance between providing multiple options, accommodating user preferences, and considering individualized factors that influence travel decisions. Metropia’s MaaS Trip Planner aims to meet these requirements, offering users a personalized and comprehensive solution for their journey planning needs.
Metropia’s MaaS Trip Planner Unique Features
Metropia’s MaaS Trip Planner is designed to cater to both multimodal and intermodal travel, encompassing a wide range of transportation options. It not only provides comprehensive door-to-door routes but also supports first/last-mile connections to public transit services. While the availability of transportation modes may vary across different cities, the trip planner’s flexible and modular design enables customization based on local offerings and data/service availability. This ensures that users have access to a diverse set of options for their journeys that combine walking, biking, ridesharing, and/or public transportation, as Figure 1 illustrates.
Figure 1: Metropia’s MaaS Trip Planner Implementation for Houston’s ConnectSmart Program (Source: TxDOT Houston’s ConnectSmart Program)
The following summarizes the unique features of Metropia’s MaaS Trip Planner and highlights how traveler-centricity is included:
Flexible Priority Settings. Allows users to adjust their priority settings to reflect available options giving them control over their preferred trade-off between fare and travel time when selecting routes.
Customizable Transfer Time. Users have the ability to specify their threshold for the amount of transfer time between different modes of transportation. This feature ensures that the planner considers realistic transfer durations, enhancing the accuracy of suggested routes.
Personalized Walking Speed. By allowing users to specify their preferred walking speed, the trip planner can accurately estimate travel times and provide routes tailored to the user’s pace.
Varied Route Output Options. The trip planner offers a range of route output options to meet different user preferences. These include earliest arrival time, shortest travel time, minimal transfer, lowest fare, shortest wait time, and the greenest routes. Users can choose the criteria that align with their specific needs and priorities.
Support for Different First/Last-Mile Travel Modes. The trip planner can incorporate shuttle/vanpools with schedule-based information obtained through GTFS (general transit feed specification), as well as location data and internal logic for local options like shared bikes. Additionally, the trip planner supports TNC or demand-responsive transit systems (DRTS) and micro-transit options through GTFS-FLEX.
Figure 2 illustrates the implementation of the personalized settings in Houston’s ConnectSmart program.
Figure 2: Metropia’s Personalized MaaS Trip Planner Settings
(Source: TxDOT Houston’s ConnectSmart Program)
System Architecture
The Metropia MaaS Trip Planner system combines data organization, efficient algorithms, and expanded network considerations to deliver comprehensive and personalized route options, as illustrated in Figure 3. By considering both public transit and first/last-mile options, it provides a seamless and convenient door-to-door trip planning experience.
Figure 3: Metropia’s MaaS Trip Planner Framework
Setting up Metropia’s MaaS Trip Planner System includes the following steps:
Public Transit & TSP Service Data. Public transit and TSP (transportation service provider) service data, including schedules, routes, and relevant information, are collected and converted into a standardized format. This data is organized and stored in internal databases for efficient processing.
Transit Network Graph Representation. The transit network graph is created using the collected data which represents the connections between various transit lines. It includes details specific to major intermodal terminals capturing realistic transfer scenarios unique to each terminal. This comprehensive representation forms the foundation for route calculations.
Computationally Efficient Algorithm. A computationally efficient algorithm is employed to calculate optimal routes based on user preferences. This algorithm takes into account factors such as time/cost tradeoff, walking time, and transfer time requirements. By efficiently evaluating different combinations and possibilities, the algorithm generates 5-20 good/pareto route options that satisfy the user's specified criteria.
Expanded Network with First/Last-Mile Options. To enhance the trip planning experience, the network is expanded to include first/last-mile options. This involves incorporating additional transportation modes such as shared services (e.g., bikes), considering factors like availability of bikes at check-out locations and rack space for return locations, as well as the time needed for check-in/out. Different time values for walking, biking, driving, and other modes are also taken into account to provide accurate estimations.
Case Study - Taiwan’s National MaaS Trip Planner
UMAJI is the MaaS project commissioned by the Ministry of Transportation and Communications (MOTC) in Taiwan and undertaken by Metropia in 2019. Despite the disruptions caused by the pandemic, Metropia successfully completed the project's deliverables, including the development of a national MaaS trip planner. UMAJI is currently fully operational.
The trip planner is a significant achievement as it incorporates Taiwan's extensive public transit network, comprising 8,800 routes, 65,000 stops, and facilitating over 200,000 daily trips. To ensure comprehensive coverage, the trip planner integrates more than a dozen public transit modes, providing users with a wide range of travel options.
Furthermore, the trip planner goes beyond public transit by incorporating several dozen first/last-mile services, both public and privately run. These include rental cars, shared micro-mobility services, and shuttle services catering to rural areas or tourist attractions. By including these additional services, the trip planner offers users seamless connectivity and access to various transportation options, enhancing their overall travel experience.
Figure 4: Taiwan’s MaaS Trip Planner Size and Complexity
Figure 5: Various First/Last-Mile Modes in Taiwan
In Taiwan, there are numerous transportation options available for traveling between Taipei, the capital city and Kaohsiung, the second-largest city. These options cater to travelers with diverse preferences and goals and thanks to the UMAJI trip planner, users can explore a range of choices tailored to their specific needs.
The UMAJI trip planner excels in delivering personalized recommendations by considering user-provided preference settings. It not only presents faster but relatively more expensive options for those who prioritize speed but also offers significantly cheaper alternatives with longer travel times for those seeking more budget-friendly alternatives. This flexibility allows users to make informed decisions based on their individual preferences and travel goals. Figure 6 illustrates an example of how Metropia’s MaaS trip planner is able to provide different route options based on distinct search objectives (travel time vs fare).
Figure 6: Example of Trip Planning with Different Time-Cost Priorities Using Metropia’s MaaS Trip Planner
Whether travelers prioritize time efficiency or cost-effectiveness, the UMAJI trip planner ensures that a variety of options are available to meet their specific requirements. By providing these alternatives, the trip planner empowers users to select the option that aligns best with their preferences, ensuring a customized and satisfying travel experience.
The successful completion of the UMAJI project showcases Metropia’s commitment to supporting transportation agencies in the advancement of MaaS solutions in Taiwan. The MaaS trip planner's comprehensive coverage and integration of diverse transportation modes contribute to creating a more efficient and sustainable mobility ecosystem for residents and visitors alike.
Metropia's planner stands out with its user-centric features, customizable options, and expandability to incorporate the latest data and services from various local providers. Metropia strives to provide a comprehensive and tailored trip-planning experience that empowers users to make informed decisions and enjoy efficient, personalized journeys.