Transit Trip Planners Research Articles

Can Trip Planner Log Files Analysis Help in Transit Service Planning?

Transit trip planners are now found on most transit authority websites. This feature gives transit users a full itinerary from a point of origin to a destination. The web server on which the trip planner is installed usually stores usage logs on a daily basis. Log files contain data on origins, destinations, calculated paths, and other website entries. The main purpose of this article is to determine whether the analysis of trip planner log files can help to improve transit service by providing better knowledge on transit users. A website-oriented analysis and a transit-oriented analysis based on four years of observations on the Montreal Transit Commission website are presented. Results show that, even though not all transit users have access to the Internet or use the planner regularly, log files can be useful for identifying new locations to be accessed by a transit system, for better understanding user behaviors, and for guiding updates of the geographic information system (GIS) and the trip planner itself.

Bike Count Forecast Model with Multimodal Network Connectivity Measures

With the rise of cycling as a mode choice for commuting and short-distance delivery, as well as policy objectives encouraging this trend, bike count models are increasingly critical to transportation planning and investment. Studies have found that network connectivity plays a role in such models, but there remains a lack of measure for the connectivity of a link in a multimodal trip context. This study proposes a connectivity measure that captures the importance of a link in connecting the origins of cyclists and nearby subway stations, and incorporates it in a negative binomial regression model to forecast bike counts at links. Representative bike trips are generated with regard to bike-friendliness using the New York City transit trip planner and used to determine the deviation from the shortest path via the designated link. The measure is shown to improve model fitness with a significance level within 10%. Insights are also drawn for income levels, bike lanes, subway station availability, and average commute time of travelers.

Fair transit trip planning in emergency evacuations: A combinatorial approach

This paper introduces the concept of proportional fair trip planning in the context of short-notice transit-based emergency evacuation. Proportional fairness attempts to meet social fairness among evacuees without sacrificing the efficiency of the evacuation process. The proportional fair trip planning concept is compared to the commonly used maximum safety concept that attempts to maximize the summation of safety functions of evacuees. We use a combinatorial approach to model the transit mass emergency evacuation in moving people from dangerous areas to safe shelters. High-density population and medium-density population variations of the problem are studied. For each variation of the problem, we study the computational complexity of the problem. We develop polynomial or pseudo-polynomial algorithms for each problem. Our numerical analysis shows that pure consideration of efficiency may result in highly unfair plans that only consider the portion of the population with the most payoffs (e.g., the population with the highest danger level) while ignoring the rest (potentially the vast majority of the population). While still considering efficiency, proportional fairness is shown to address this issue by also allocating resources to the population that has non-optimal payoffs.

Subway user behaviour when affected by incidents in Toronto (SUBWAIT) survey — A joint revealed preference and stated preference survey with a trip planner tool

Transit user behavioural response under disrupted service conditions, specifically how transit riders choose among available mode options to complete their trips, is not well understood. This study aimed to investigate transit user mode choice in response to rapid transit service disruption in the City of Toronto, incorporating such factors as the type of disruption, stage of the passenger’s trip (pre-trip or en-route), weather conditions, and uncertainty of delay duration. A joint revealed preference (RP) and stated preference (SP) survey was designed where the RP part gathered information on the respondent’s actual response to the most recent service disruption while the SP part solicited the respondent’s travel choices under a set of hypothetical service disruption scenarios. A transit trip planner tool was developed to generate alternative transit mode and path options to avoid the disrupted segment. An empirical model using RP data is presented to verify the survey design technique.

A normative optimal strategy in intelligent transit networks

Abstract The paper focuses on unreliable dynamic transit service networks, on which, even if predictive info is available, trip planners should give dynamic strategy-based path suggestions, rather than provide a complete path up to destination. In the paper, the search for a travel strategy to be used as a path recommendation in innovative transit trip planners is analysed as a Markov decision problem, together with the relative solution approaches. Applicative examples of the procedure on a simple test network are then reported. Finally, some concluding remarks are made and future research perspectives outlined.

A reliability-based transit trip planning model under transit network uncertainty

Transit, although an important public transportation mode, is not thoroughly utilized in the United States. To encourage the public to take transit, agencies have developed systems and tools that assist travelers in accessing and using information. Transit data modeling and trip planner system architecture developments have helped advance these systems, and the recent emergence of transit trip planning algorithms promises further enhancement. Conventional transit trip planning algorithms are usually developed based on graph theory. In order to utilize these algorithms, certain assumptions must be made to support these algorithms (e.g. buses always run on time). However, these assumptions may not be realistic. To overcome these limitations, our study develops an innovative transit trip planning model using chance constrained programming. Unlike previous studies, which only minimized passenger-experienced travel time, our study also considers transit service reliability. Additionally, in-vehicle travel time, transfer time, and walking time are all included as elements of passenger-experienced travel time. Our transit trip planning model avoids the assumptions of previous studies by incorporating transit service reliability and is capable of finding reliable transit paths with minimized passenger-experienced travel time. The algorithm can also suggest a buffer time before departure to ensure on-time arrivals at a given confidence level. General Transit Feed Specification data, collected around Tucson, Arizona, was used to model the transit network using a “node-link” scheme and estimate link-level travel time and travel time reliability. Three groups of experiments were developed to test the performance of the proposed model. The experiment results suggested that the optimal anticipated travel time increased with increasing on-time arrival confidence level and walking was preferred over direct bus transfers that involved out of direction travel. The proposed model can also include additional travel modes and can easily be extended to include intercity trip planning.

Individual utility‐based path suggestions in transit trip planners

The opportunities offered by telematics development and the features required by travellers moving on more complex multimodal transit networks push to investigate new methods for generating path advice in transit trip planners. First, the study focuses on characteristics and limits of the methods used by current trip planners for path generation and then analyses the new methods applied by a new generation of trip planners (most at the prototypical developing stage). These methods use a group or, better, an individual traveller utility function, which allows personal preferences to be pointed out. As the individual utility functions in the transport modelling literature has been largely neglected, the second part of the study reports the state-of-the-art and some theoretical considerations on individual path utility function modelling, and recalls the approaches developed for including individual utility in new trip planners. Further, the results of individual utility function estimations, using surveys relative to the city of Rome, are presented and some aspects of the utility function calibration, including the info provider learning process of traveller preferences, are explored. The analyses show a significant improvement in using individual functions for path advice and a substantial influence of network complexity on the learning process.

Multimodal Public Transit Trip Planner with Real-time Transit Data

Trip planner is a smart travel assistance tool which provides pre-trip travel plan information to the commuter for the given origin and destination stops. It is built-up with information such as road network and vehicles’ schedule from various transport agencies. The schedules are static and deviates much in actual due to delays caused by dwell time, traffic congestion and accidents etc. Therefore, many real-time trip planners were developed to produce more accurate plans in terms of time and transit vehicles which use data from Global Positioning System (GPS) enabled vehicles. However, the methodologies for vehicle positioning in real-time, dealing with interrupted GPS signals and just-in-time update of transit network is still not crystallized. Mostly the vehicle positioning is handled by a third party. In addition, most of the real-time planners do not consider arterial routes. In this paper we present development of a multimodal intracity transit trip planner using public transport. It incorporates the delays into the transit network at real-time to minimize the gap of our prediction model. K- shortest path algorithm is being used to compute multi-criterion optimal plans. Here we consider both delays in all routes including arterial ones and frequently missing GPS signals. Our system handles all these with some heuristics and simulation. Besides the web interface, trip planning can also be done by sending Short Messaging Service (SMS) from any mobile phone to the server. The results have shown that the system produces plans which are acceptable in terms of response time, feasibility and accuracy.

Leveraging the General Transit Feed Specification for Efficient Transit Analysis

Since 2007, the transit industry has benefited from a widely adopted data standard called the General Transit Feed Specification (GTFS), which has enabled the development of numerous traveler information tools (i.e., transit trip planners). The purpose of this project is to demonstrate the potential for GTFS feeds as a data source for transit analyses, such as those found in the Transit Capacity and Quality of Service Manual. There are three primary project tasks: an analysis of GTFS field usage by different agencies; an analysis of a single agency's operations at the stop, route, and system levels; and a batch analysis and comparison of 50 large transit agencies in North America. Compared with manually transcribing schedules from transit websites or parsing printed schedules, the use of scripts and database queries suggests that the GTFS is a highly efficient data source and proves the importance of broadly accepted data standards. The methodology documented in this paper and the open source scripts (made available online) will be useful for any analyst or researcher who has tasks related to the analysis of single or multiple transit systems at the stop, route, or system level.

A Multimodal Trip Planning System With Real-Time Traffic and Transit Information

Most transit trip planning systems are based on static schedules and generate trips that do not dynamically respond to delays in transit operation caused by traffic congestion or accidents. In addition, the driving-parking-then-transit travel mode is common in metropolitan areas. However, very few transit trip planners incorporate real-time transit data into this mode. This article describes a multimodal trip planning system for multiple modes: driving, transit, and driving-parking-then-transit. The system considers the real-time transit arrival time, which is estimated by a prediction model. Both Web-based and mobile phone-based clients are used to access the system. Case studies show that the multimodal trip planning system works well in real-life situations.

Development of Web-Based Transit Trip-Planning System Based on Service-Oriented Architecture

The majority of transit trip planners exist as proprietary systems based on particular vendor products. With the incorporation of more functional components, system maintenance and regular transit information updates become burdensome tasks for transit agencies. In addition, the proprietary nature of the systems makes it difficult to take advantage of the rapid advancement of geospatial information and web technologies. The authors proposed an open and interoperable transit trip-planning system based on a service-oriented architecture, with the principle of reusing the existing modular resources, while providing user-friendly interfaces for expansion of functionality. The objective was to integrate geospatial services available online (such as Google Maps), open-source geospatial database technologies, and path-finding algorithms in a loosely coupled manner. The proposed system was developed with spatial and temporal transit data from Waukesha Metro Transit in Wisconsin. Research results were validated by comparing outputs from the existing South-East Wisconsin Transit Trip Planner and route schedule matching. Comparison results showed that the new service-oriented architecture provided a flexible, efficient mechanism for transit-trip planners. The architecture took advantage of rapidly changing online geospatial services, yet maintained the core functions of itinerary search that may be unique to each transit agency.

The multimodal trip planning system of intercity transportation in Taiwan

The multimodal trip planning system (MTPS) described in this paper can efficiently identify reasonable journeys. Our methodologies could be implemented in future transit trip information systems. An effective MTPS would be of value to transit agencies or administrators due to the large number of different yet related characteristics of the transit network. In Taiwan, no successful MTPS exists owing to the fact that there are many intercity transit options, including airlines, ships, intercity buses, rail, high-speed rail, local buses, and mass rapid transit. The purpose of this study is to develop an MTPS that will ensure that transit stops are accurately sequenced and integrated into appropriate route patterns, including schedule and route data, as well as intermodal transfer locations and multi-agency transit center walking directions. The MTPS will provide landmark-to-landmark journey planning for transit users. We combine a data structure and multimodal trip planning algorithm in the MTPS that can swiftly propose reasonable travel plans. Finally, we develop a transit trip planning system for Taiwan.

Modeling transit networks by GML for distributed transit trip planners

Transit networks and their components are complex spatiotemporal features. To support advanced applications such as distributed transit trip planners, an efficient data model for representing, encoding and sharing complex features is indispensable. This paper presents a transit network data model with GML schemas for data encoding and sharing. The data model and schemas are implemented in an experimental distributed transit trip planner composed of two independent transit agencies and a distributed trip planning engine. Effectiveness of the data model and schemas are demonstrated by the implementation. The paper also addresses issues related to complex features modeling and data sharing by GML.

A spatiotemporal data model for dynamic transit networks

A transit network is a spatiotemporally dynamic system whose topology, components, and services all change over time. The complexity and dynamic nature of transit networks make it difficult to model by traditional transportation models. This paper presents an object‐oriented data model that coherently represents space, time, and dynamics of transit networks and supports schedule‐based pathfinding. In this data model, a transit network and its components are modelled as spatiotemporal objects that have properties and behaviours. Unlike traditional roadway transportation network models that define topology as connectivity between geometric elements such as edges and nodes, this data model uniquely defines topology as sequences and collections of transit features which are application domain objects. Moreover, this data model supports the generation of active network topologies based on given times to represent real‐time network states as well as to reduce topological redundancy. Implementations of the data model in two Internet transit trip planners have demonstrated that it can effectively reduce computation complexity of schedule‐based pathfinding algorithms and enhance pathfinding performance.

A Standard-Based Integration Framework for Distributed Transit Trip Planning Systems

In the United States, most current transit trip planning systems on the Web are application silos. That is, they are difficult or even impossible to use when planning a trip across transit systems. The problem is especially severe when two transit agencies use different trip planning systems. This article describes a framework to integrate different trip planning systems into a distributed yet seamless system. It uses Web Services and XML technologies, and adopts the advanced traveler information systems standard developed by the Society of Automotive Engineers to exchange transit itinerary information between systems. This integration framework supports loose coupling, expandability, and standardization. It allows individual transit agencies to maintain and update their own data and transit trip planning systems, but uses a mediator server to integrate them in a seamless manner. The end user can plan transit trips that cross different transit system boundaries seamlessly.

A Schedule-based Pathfinding Algorithm for Transit Networks Using Pattern First Search

The lack of effective and efficient schedule-based pathfinding algorithms for transit networks has limited the application of GIS in transit trip planning services. This paper introduces a schedule-based path finding algorithm for transit networks. Based on a pattern-centered spatiotemporal transit network model, the algorithm searches the network by following route patterns. A pattern is a spatial layout of a route in transit terminology. A route usually has many patterns to serve various locations at different times. This path search algorithm is significantly different from traditional shortest path algorithms that are based on adjacent node search. By establishing a set of lemmas and theorems the paper proves that paths generated by the PFS algorithm are schedule-coordinated fastest paths for trips with given constraints. After analyzing computation and database query complexities of the algorithm the paper indicates that the PFS is efficient in computation and database query. Finally, effectiveness and efficiency of the algorithm are demonstrated by implementations in GIS-based online transit trip planners in Wisconsin, US.