Multiple Travel Modes Research Articles

A Rational Agent Model for the Spatial Accessibility of Primary Health Care

Accurate modeling of the spatial accessibility of health care is critical to measuring and responding to physician shortages. We develop a new model in which patients choose the primary care location that minimizes their combined accessibility and availability costs. This model offers several advantages with respect to existing access frameworks. It incorporates feedback between patient decisions and endogenizes the trade-off between travel times and congestion at the point of care. It allows for patients to seek care from their home or workplace and can account for multiple travel modes. Our open-sourced implementation scales efficiently to large areas and fine spatial granularity. Using distributed computing, we calculate travel times for this model at the census tract level for the entire United States, and we also make this resource available. We compare the results to those from existing primary care access models. Key Words: floating catchment areas, primary health care, rational agent models, spatial accessibility.

Multimodal travel-based multitasking during the commute: Who does what?

Research on multitasking during travel has increased, yet most studies have focused on public transit passengers. This work investigated travel-based multitasking and its potential determinants across multiple travel modes (automobile, nonmotorized, and transit) using a 2016 survey of around 650 commuters in the Portland, Oregon, region. First, exploratory factor analysis on 23 activities yielded two activity groups: those related to information and communications technologies (texting/emailing/messaging, reading electronically, and using social websites/apps) and those that are more passive in nature (viewing scenery or people watching and thinking/daydreaming). Next, mode-specific binary logit models predicted participation in each activity (or activity group) as a function of travel time, traveler socio-demographics, perceptions, and other factors. Travelers using riding modes (transit riders and auto passengers) engaged in a greater number and variety of activities, while vehicle (auto and bicycle) operators did fewer and more passive activities. For walking/bicycling commuters, exercising or being physically active appeared to be an enjoyable and productive use of travel time. On the other hand, many instances of travel-based multitasking for transit passengers may be more about coping with a burdensome or boring commute or the result of being time poor. These findings offer important implications for understanding motivations for travel-based multitasking, sustainable transportation mode choices, and future demands for autonomous vehicles.

Integrating multiple transportation modes into measures of spatial food accessibility

IntroductionPeople can access to healthy food via different modes of transportation, such as traveling by car, transit, bicycle and foot. We categorize current measures of food accessibility under an origin-destination-mode framework and find that few of them integrate multiple travel modes. As a result, these measures can bias the identification of truly low-access areas. MethodsTo fill this gap, we propose two new measures that integrate sub-populations of various travel modes, and estimate the overall food accessibility of a whole population. Taking Florida, USA, as a study area, we illustrate our measures with actual multiple mode commuting data from the U.S census transportation planning products (CTPP). We then compare the results to those from conventional single-modal measures. ResultsThe proposed multiple-mode measures tend to estimate a larger population with low accessibility and fewer accessible supermarkets for a census tract, as compared to single-mode measures. The incorporation of multiple travel modes into food accessibility measures also narrows the disparities between urban and rural areas, which are indicated by conventional measures. ConclusionsBy considering modal-split subpopulations, our measures offer a more realistic representation of local people's travel for grocery shopping, and thus a better identification of populations with low food access. The finer modeling scale at a subpopulation level provides health and urban planners more flexibility in policy design, in that interventions can be tailored to not only a neighborhood but also a specific subpopulation within it. Such knowledge could improve the cost-effectiveness of food intervention programs.

Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes

For a mobile robot, navigation skills that are safe, efficient, and socially compliant in crowded, dynamic environments are essential. This is a particularly challenging problem as it requires the robot to accurately predict pedestrians’ movements, analyse developing traffic situations, and plan its own path or trajectory accordingly. Previous approaches still exhibit low accuracy for pedestrian trajectory prediction, and they are prone to generate infeasible trajectories under complex crowded conditions. In this paper, we develop an improved socially conscious model to learn and predict a pedestrian’s future trajectory. To generate more efficient and safer trajectories in a changing crowed space, an online path planning algorithm considering pedestrians’ predicted movements and the feasibility of the candidate trajectories is proposed. Then, multiple traffic states are defined to guide the robot finding the optimal navigation strategies under changing traffic situations in a crowded area. We have demonstrated the performance of our approach outperforms state-of-the-art approaches with public datasets, in low-density and simulated medium-density crowded scenarios.

Understanding the Effects of Improving Transportation on Pilgrim Travel Behavior: Evidence from the Lhasa, Tibet, China

Effective travel demand management measures provide the opportunity to fully utilize limited transportation resources, especially in underdeveloped areas. It is increasingly recognized that the improvement in existing transportation infrastructure and the optimization of traffic demand management method would result in a complicated urban transportation system with multiple travel modes. This paper aims to investigate the relationship between transportation improvements (e.g., pedestrian flow, free bus for the elderly, and parking space planning) and the mode choice behavior of pilgrimages in the Lhasa of Tibet, China. This study employed a distinctive survey conducted among pilgrims in Lhasa, including both individual questionnaires and interviews from 2010 to 2016. The analysis was undertaken using a multinomial logit model to identify the extent to which transportation improvements could affect the pilgrim’s travel mode choice behavior. The results show that transportation improvements, as an operational method in underdeveloped areas, play an important role in motivating the pilgrimage to travel that can increase the attractiveness of private car use, and make pedestrian traffic more prominent. However, improvements in the public transport need to be conducted to attract more travelers. These results confirm that increasing the attractiveness of low-carbon transportation (e.g., buses, walking, and cycling) to the public can reduce the usage on private vehicles and maintain the development of sustainable transportation in underdeveloped areas with limited transportation resources.

Adolescent school travel: Is online mapping a practical alternative to GPS-assessed travel routes?

Abstract Background Geographically accurate travel routes are necessary to estimate exposure to the environment and its potential influence on travel behaviour. Although assessing travel behaviours with Global Positioning System (GPS) receivers is increasingly common, these protocols place noticeable burden on participants and processing these data is time consuming and error-prone. Interactive online mapping surveys allow users to draw their own travel routes, and may offer a time and cost-effective alternative; however, these routes are still self-reported, and their true accuracy remains unknown. Methods A total of 196 adolescents drew their usual route to school within an online mapping survey and wore a GPS receiver for 7 days. Individual home-to-school routes were extracted from GPS data. Generalized linear mixed models were used to assess differences in distance and spatial agreement between routes, and how these varied by mode of travel and other route characteristics. Results GPS-assessed routes were longer than the routes participants drew across all travel modes. Routes travelled actively displayed 12.32% higher agreement compared to those travelled passively (p Conclusions Online mapping surveys are a feasible method for route assessment in adolescents, particularly for active travel routes. With the integration of survey questions, there is considerable potential for understanding the intricacies of travel behaviours. However, the self-reporting error seems more pronounced for longer routes, and when multiple travel modes are used. Researchers should consider the advantages (e.g., ease of collection) and disadvantages (e.g., lack of temporal information) when deciding if the data obtainable are sufficient to answer their research questions.

Online Travel Mode Identification Using Smartphones With Battery Saving Considerations

Personal trips in modern urban society usually involve multiple travel modes. Recognizing a traveler's transportation mode is not only critical to personal context awareness in related applications but also essential to urban traffic operations, transportation planning, and facility design. While most current practice often leverages infrastructure-based fixed sensors or a Global Positioning System (GPS) for traffic mode recognition, the emergence of the smartphone provides an alternative promising way with its ever-growing computing, networking, and sensing power. In this paper, we propose a GPS-and-network-free method to detect a traveler's travel mode using mobile phone sensors. Our application is built on the latest Android platform with multimodality sensors. By developing a hierarchical classification method with an online learning model, we achieve almost 100% accuracy in a binary classification of wheeled/unwheeled travel modes and an average of 97.1% accuracy with all six travel modes (buses, subways, cars, bicycling, walking, and jogging). Our system (a) could adapt to each traveler's pattern by using the online learning model, and it performs significantly faster in computation than the offline model, and (b) works with a low sampling frequency for sensing so that it saves the smartphone battery.

Use of different exposure metrics for understanding multi-modal travel injury risk

Abstract The objective of this work is to identify characteristics of different metrics of exposure for quantifying multi-modal travel injury risk. First, a discussion on the use of time-based and trip-based metrics for road user exposure to injury risk, considering multiple travel modes, is presented. The main difference between a time-based and trip-based metric is argued to be that a time-based metric reflects the actual duration of time spent on the road exposed to the travel risks. This can be proven to be important when considering multiple modes since different modes typically different speeds and average travel distances. Next, the use of total number of trips, total time traveled, and mode share (time-based or trip-based) is considered to compare the injury risk of a given mode at different locations. It is argued that using mode share the safety concept which focuses on absolute numbers can be generalized. Quantitative results are also obtained from combining travel survey data with police collision reports for ten counties in California. The data are aggregated for five modes: (i) cars, (ii) SUVs, (iii) transit riders, (iv) bicyclists, and (v) pedestrians. These aggregated data are used to compare travel risk of different modes with time-based or trip-based exposure metrics. These quantitative results confirm the initial qualitative discussions. As the penetration of mobile probes for transportation data collection increases, the insights of this study can provide guidance on how to best utilize the added value of such data to better quantify travel injury risk, and improve safety.

Characterizing Passenger Flow for a Transportation Hub Based on Mobile Phone Data

As the vital node of a passenger transportation network, the transportation hub is the connection between multiple travel modes and the important port for the massive passenger flow to enter into or exit from a city area. Transportation operators need to understand the passenger flow pattern for hub management, transportation planning, and so on. However, it is difficult to use traditional methods, such as video detection, to provide such information. With the increasing number of mobile phone users, mobile phone data have shown remarkable potential in detecting the transportation information with high sampling coverage and low cost. This paper utilizes the mobile phone data to characterize the passenger flow of the Hongqiao transportation hub located in Shanghai, China. First, a temporal-spatial clustering method is proposed to identify the passenger active area of the Hongqiao hub in the wireless communication space. Second, a classification process is presented to extract different types of passengers in this transportation hub. Subsequently, the access characteristics of passengers in the city are studied for various time intervals. The results further verify the potential of using mobile phone data to monitor and characterize passenger flow related to the transportation hubs.

When a Diet Prompts a Gain: Impact of a Road Diet on Bicycling in Davis, California

With the goal to improve the integration of multiple travel modes into traditional roadway designs, many jurisdictions have considered road diets, characterized by reductions in vehicular traffic lanes and reallocation of right-of-way for other modes. Studies show that road diets can improve safety without slowing automobile traffic, but benefits for pedestrians and bicyclists have not been widely documented. To address this gap, this study examined the effects of a road diet project in Davis, California. Data were collected on the number of bicyclists and pedestrians at key intersections and automobile travel times along the corridor before and after the road diet treatment. Every intersection that was studied experienced a statistically significant increase in the number of bicyclists during the morning or the evening peak period or during both. On average across all intersections studied along the corridor, the number of bicyclists who used Fifth Street increased by 243%, but the change in pedestrian volume was not statistically significant. Contrary to common fears about road diets, automobile travel times decreased a statistically significant amount during the evening peak. This study demonstrated that road diets can benefit bicyclists without negative effects on automobile travelers.

Supporting Adaptive Tour with High Level Petri Nets

One of the issues for tour planning applications is to adaptively provide personalized advices for different types of tourists and tour activities. This paper proposes a high level Petri Nets based approach to providing some level of adaptation by implementing adaptive navigation in a tour node space. The new model supports dynamic reordering or removal of tour nodes along a tour path; it supports multiple travel modes and incorporates multimodality within its tour planning logic to derive adaptive tour. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper.

Social Welfare Maximization of Multimodal Transportation

Multimodal urban transportation systems exhibit complex inter actions between components, including users, multimodal transportation facilities, supply side agencies, and operators. Although these interactions are obvious, rigorous quantitative methods for optimizing control variables across modes of transportation on real-world networks are deficient. A social welfare maximization model was established for joint optimization of bus fare, rail transit fare, and congestion tolls for private cars. The authors determined the optimality conditions and the second-order partial derivatives for this optimization problem. Because of the complexity of the multimodal urban transportation system, the objective function of social welfare has no closed form and is extremely expensive to evaluate. The authors therefore proposed a simulation-based framework for evaluation of the objective function and optimization of three decision variables across multiple travel modes. Several metamodels (i.e., mathematical functions that approximate the true shape of an unknown, nonlinear, and complex objective function) were adopted to approximate the highly nonlinear input–output mappings in the urban system. This is the first study to develop a simulation-based method for joint optimization of transit and road network operations. The case study applied the simulation-based optimization framework to the Sino–Singapore ecocity in Tianjin, China, by using VISSUM as the urban systems simulator. An “ecocity” is defined by the authors as “a thriving city that is socially harmonious, environmentally friendly, resource-efficient, and a model for sustainable development.” Results show that metamodels can accurately approximate the real objective function and produce good suboptimal and near-optimal solutions. The optimal combination of transit fares and congestion tolls significantly outperform those under two baseline scenarios. The optimal solutions also suggest that extreme transit fares (too high or too low) or congestion tolls are contrary to welfare-maximizing objectives. The proposed method can be applied for joint optimization of other multimodal planning and operational strategies, such as investment and operational decisions across various modes of transportation.

Traffic reallocation impacts and automobile toxic pollutants emission for a general network in urban highway system: a second-best congestion pricing analysis

This paper examines temporal, spatial and modal effects of second-best congestion pricing schemes in traffic volume reallocation, traffic mode shifting and automobiles toxic pollutants emission control in urban road systems. A problem in two time periods (peak and off-peak) and two travel modes (private car and bus) on one-way network is analysed, and a second-best congestion pricing model with multiple time periods and travel modes is constructed using an excess-demand approach. The paper provides a thorough empirical study of second-best scenario and two extreme regimes: no-toll problem and first-best problem. The main findings are: congestion pricing policies have major impacts: 1) diversion of tolled route to the free route; 2) shift of peak period traffic to the off-peak period; 3) shift of traffic volume by private car to bus; 4) reduction in total traffic volume and increase in total passenger volume (private car and bus); 5) reduction in the total emission amount of automobiles’ toxic pollutants.

Complete Enough for Complete Streets?

An increasing number of jurisdictions across the United States are exploring level of service (LOS) for multiple travel modes, in part as a result of the release of the Highway Capacity Manual 2010 (HCM 2010), as well as an increased focus on complete streets policies. One of the most important questions being asked by these jurisdictions is whether new multimodal LOS methods are sensitive enough to inform transportation investments, mitigate impacts, and prioritize future projects. For this paper, transportation professionals (public, private, and academic) were surveyed about the inputs believed to have the greatest effect on pedestrian, bicycle, transit, and auto LOS or the inputs thought to have the greatest likelihood of being changed (e.g., to mitigate an impact or to improve existing conditions). Sensitivity testing was then performed at locations in four cities to measure how the HCM 2010 multimodal LOS scores responded as these inputs were incrementally increased or decreased. Although many inputs performed as expected, the testing also found model responses that were of a questionable direction or magnitude. The results of this study are informative for agencies that are considering adopting the HCM 2010 multimodal LOS for mitigation, resource allocation, and strategic decision making. The results also provide a starting point for additional research needed to enhance multimodal LOS methods.

PAMSCOD: Platoon-based arterial multi-modal signal control with online data

A unified platoon-based mathematical formulation called PAMSCOD is presented to perform arterial (network) traffic signal control while considering multiple travel modes in a vehicle-to-infrastructure communications environment. First, a headway-based platoon recognition algorithm is developed to identify pseudo-platoons given probe vehicles’ online information. It is assumed that passenger vehicles constitute a significant majority of the vehicles in the network. This algorithm identifies existing queues and significant platoons approaching each intersection. Second, a mixed-integer linear program (MILP) is solved to determine future optimal signal plans based on the current traffic controller status, online platoon data and priority requests from special vehicles, such as transit buses. Deviating from the traditional common network cycle length, PAMSCOD aims to provide multi-modal dynamical progression (MDP) on the arterial based on the probe information. Microscopic simulation using VISSIM shows that PAMSCOD can easily handle two common traffic modes, transit buses and automobiles, and significantly reduce delays for both modes under both non-saturated and oversaturated traffic conditions as compared to traditional state-of-practice coordinated-actuated signal control with timings optimized by SYNCHRO.

PAMSCOD: Platoon-based Arterial Multi-modal Signal Control with Online Data

A unified platoon-based mathematical formulation called PAMSCOD is presented to perform arterial (network) traffic signal control while considering multiple travel modes in a vehicle-to-infrastructure communications environment. First, a headway-based platoon recognition algorithm is developed to identify pseudo-platoons given probe vehicles’ online information. It is assumed that passenger vehicles constitute a significant majority of the vehicles in the network. This algorithm identifies existing queues and significant platoons approaching each intersection. Second, a mixed-integer linear program (MILP) is solved to determine future optimal signal plans based on the current traffic controller status, online platoon data and priority requests from special vehicles, such as transit buses. Deviating from the traditional common network cycle length, PAMSCOD aims to provide multi-modal dynamical progression (MDP) on the arterial based on the probe information. Microscopic simulation using VISSIM shows that PAMSCOD can easily handle two common traffic modes, transit buses and automobiles, and significantly reduce delays for both modes under both non-saturated and oversaturated traffic conditions as compared to traditional state-of-practice coordinated-actuated signal control with timings optimized by SYNCHRO.

Can information promote transportation-friendly location decisions? A simulation experiment

Where people live, work, shop, and recreate fundamentally determines their local travel options. Yet, information problems such as the cost of conducting comprehensive searches and cognitive load have been shown to limit decision-making. In the context of residential decision-making, information problems are likely to influence which locations get chosen. This study examines whether providing people seeking a rental home with map-based information about the transit and pedestrian accessibility of the available units might influence their residential location choices. More specifically, would some people make use of this information to select more accessible residences than they would have otherwise chosen? This proposition was tested through an experimental research design in a laboratory setting. Graduate student participants were asked to select their top choices of where to live after reviewing a database of residential properties custom-designed for this study. In order to assess the influence of accessibility information, we divided participants randomly into control and experimental groups, with the former receiving baseline information currently available and the latter receiving map-based supplemental property information on multi-modal accessibility to desirable destinations. The study results suggest that providing multi-modal accessibility information to people who are relocating will enhance the attractiveness of locations that support multiple travel modes. If this is true for broader populations, then planners and policy makers may be able to increase use of non-auto modes by providing multi-modal transportation information to people at the time when they are looking for a new residence.

Generalized Approach for Assessing Direct User Impacts of Multimodal Transport Projects

The effects of a transportation project can be estimated either by benefit–cost accounting or by economic development impact analysis; both are regularly used by government agencies to plan and justify investments. Although these approaches differ in methodology, both rely on the estimation of direct user impacts—those arising from changes in the quantity and quality of travel. However, a survey of published analyses and guides reveals, first, that there is considerable confusion between an economic benefit (as used in benefit–cost accounting) and an economic impact (as used in impact analysis) and, second, that user impacts are frequently estimated too narrowly by ignoring intermodal effects and impacts to freight shippers. To address these issues, a framework is presented for estimating direct user impacts that is generalized to accommodate multiple travel modes in a way that avoids double counting, gives a full treatment of freight shippers and carriers, and differentiates between benefits and impacts for use in either type of analysis.

Solving network equilibrium problems on multimodal urban transportation networks with multiple user classes

A modelling approach for solving quite general network equilibrium problems (with fixed trip productions and attractions) intrinsic to the urban transport planning process is presented. The framework can consider a variety of demand models and route choice behaviours within the same implementation, including multiple user classes and combined travel modes that interact on the same physical network. The demand choices are assumed to have a hierarchical structure. When trip distribution is variable, a doubly constrained entropy‐maximizing model is considered at the first level of choice and a hierarchical logit model is used for the remaining demand choices (time of departure, travel mode, transfer point for combined modes, etc.). If trip distribution is considered to be exogenous, the demand choices are modelled as a hierarchical logit. One of the main features of the model is that it considers the effects of congestion on the road network as well as congestion and capacity constraints effects in each public transport service network. The problem is formulated mathematically as a variational inequality, with asymmetric cost functions, and solved following the diagonalization procedure. Each iteration of the aforementioned procedure solves an optimization problem using the Evans algorithm. Sufficient conditions for the existence and uniqueness of the solution to the diagonalized problem are obtained. The main results of a simple example (solved with an academic version of the proposed algorithm) are presented to show the consistency of the equilibrium flows and levels of services obtained using the model. Finally, a real scale implementation of the model is briefly described to show the feasibility of its application.

Accessibility Analyst: An Integrated GIS Tool for Accessibility Analysis in Urban Transportation Planning

The authors present an integrated GIS tool, Accessibility Analyst, for accessibility analysis in urban transportation planning, built as an extension to the desktop GIS software package, ArcView. Accessibility Analyst incorporates a number of accessibility measures, ranging from catchment profile analysis to cumulative-opportunity measures, gravity-type measures, and utility-based measures, contains several travel-impedance measurement tools for estimating the travel distance, time, or cost by multiple travel modes along actual travel routes, and interoperates with GIS data-management and data-integration, spatial-analysis, network-analysis, surface-modelling, and spatial-visualisation functions. Undertaking accessibility analysis with use of Accessibility Analyst allows the user to take full advantage of a GIS to produce spatial distributions of accessibility over a region. It can be applied to a wide range of issues in urban transportation planning, such as for studies on the relationship between transportation and land use, evaluation of transportation network efficiency, transportation infrastructure planning, and for impact assessments relating to transportation policies.