Measures to evaluate post-disaster trip resilience on road networks

The resilience of transportation networks, one of the most critical infrastructure in post-disaster situations, will have a significant influence on post-disaster operations, community resilience and business continuity. Consequently, understanding the resilience of transportation networks following a natural disaster is crucial. This research proposes a new Trip Resilience (TR) measure to assess the resilience of trips on road networks following a disaster, integrating all three dimensions of resilience, namely robustness, redundancy, and recovery. The methodological approach includes an analysis of existing transport resilience measures presented in the literature to assess their ability to quantify robustness, redundancy and recovery in terms of the proposed conceptual model. The analytical formulations of the individual component measures are then developed, or adapted from previous research, along with a means of integrating all three into a combined Trip Resilience (TR) measure. A case study methodological approach is then adopted to verify the practicality of the proposed measures using the outcomes from a transportation simulation of a hypothetical Alpine Fault Magnitude 8 (AF8) scenario. A Normalised Trip Resilience (NTR) measure is also proposed that converts the TR to a normalised scale that is easily understandable to decision-makers. Finally, in order to facilitate ranking of the post-disaster impact on districts, a new measure, namely the Equivalent daily number of Impacted Trips (EIT), is proposed. The proposed measure provides an opportunity for decision-makers to estimate and rank the trip resilience between each (group of) Origin-Destination pair(s) using pre- and post-disaster flow and travel time. The resulting measures were capable of being calculated from the outputs produced by the transportation simulation model in the case study, thereby verifying their practicality in real-world situations. The importance of including both robustness (represented by the number of eliminated trips) and redundancy (represented by increased travel time), over the horizon of the post-disaster recovery phase was highlighted. Eliminated trips contributed significantly in areas that were cut off and isolated post-disaster, due to a lack of alternative routes, and increased travel time contributed as more roads were reopened but the alternative routes resulted in increased travel distances and, consequently, travel time.     

Importance measures for inland waterway network resilience

This work demonstrates a time-dependent paradigm for resilience and associated stochastic metrics in a waterway transportation context. We deploy two stochastic resilience-based component importance measures that highlight the critical waterway links that contribute to waterway network resilience and develop an optimization approach that determines the order in which disrupted links should be recovered for improved resilience. A data-driven case study illustrates these metrics to describe commodity flows along the various links of the US Mississippi River Navigation System.     

Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai,China

In recent years, dockless bike-sharing has rapidly emerged in many cities all over the world, which provides a flexible tool for short-distance trips and interchange between different modes of transport. However, new problems have arisen with the fast and extensive development of the dockless bike-sharing system, such as high running expenses, ineffective bike repositioning, parking problems and so on. To improve the operations of the dockless bike-sharing system, this study aims to investigate the travel pattern and trip purpose of the bike-sharing users by combining bike-sharing data and points of interest (POIs). A massive amount of bike-sharing trips was obtained from the Mobike company, which is a bike-sharing operator in China. The POIs surrounding each trip origin and destination were derived from the Gaode Map application programming interface. K-means++ clustering was adopted to investigate dockless bike-sharing travel patterns and trip purpose based on trip records and their surrounding POIs. The clustering results show that on weekdays, bike-sharing trip origin and destination can be divided into five typical groups, i.e., dining, transportation, shopping, work and residential places. Dining is the most popular trip purpose by bike-sharing, followed by the transferring to other transportation modes and shopping. In addition, through understanding the spatial distribution of the bike-sharing usage patterns of five typical activities, strategies for improving the operation of the dockless bike-sharing system are provided.     

Regional transportation planning in the US: An examination of changes in technical aspects of the planning process in response to changing goals

This paper presents a framework for examining the ways in which technical aspects of the regional transportation planning process are changing in response to a broadening of goals and strategies, driven in part by an increased emphasis on public involvement. As an illustration of this framework, the long-range plans of four Metropolitan Planning Organizations (MPOs) in the US are analyzed with respect to goals, performance measures, and use of forecasts. These examples suggest that changes in the technical aspects of the process are lagging changes in planning goals. As a result, plans may implicitly emphasize congestion relief even if they put forth a much broader range of goals. This paper provides a basis for further analysis of the current evolution of transportation planning in the US.     

Survey of E-scooter users in Vienna: Who they are and how they ride

In many cities around the world, electric (e-)scooters have emerged as a new means of transportation. They are often advertised as supporting modal shift towards more sustainable transportation and as a tool for enabling more equity in mobility. However, the environmental impact depends on how they are used and what kinds of trips they replace. Integration of e-scooters into urban transport systems also implicates discussions on fair road space allocation. In our study, we assess the socio-economic profiles and usage patterns of e-scooter users in Vienna, Austria. We differentiate between two basic groups of e-scooter users (renters and owners) and apply two different methods. Firstly, based on an online survey, we examine the age, gender and education of e-scooter users and we look into which kinds of trips (commuting, shopping or leisure) and which other means of transportation are replaced by e-scooter trips. Secondly, we analyse data from field observations at cycle paths in Vienna in order to determine the share of e-scooter riders and their gender distribution. We find that e-scooter users are more likely to be young, male, highly educated and residents of Vienna. According to the survey, there are considerable differences in usage between owners of private scooters and users of sharing schemes. Whereas in both groups, e-scooter trips mostly replace walking and public transport as a mode, e-scooter owners also show a considerable mode-shift from private car trips. These results implicate that e-scooter riders are additional users of cycling infrastructure. This puts further pressure on the current allocation of road space, which provides little space for active modes of transport. We conclude that city policies should address this competitive relationship adequately by allocating more space to safe and convenient cycling infrastructure and traffic-calmed zones. This could not only help ease the current challenges due to e-scooters but also provide better conditions for walking and cycling and thereby at the same time contribute to a more sustainable and equitable urban transport system.     

The role of airport proximity on warehouse location and associated truck trips: Evidence from Toronto,Ontario

This paper examines the two-way relationship between land use and transportation from the perspective of warehouses in the Toronto Census Metropolitan Area. A location choice model is first developed to reproduce the decision process of firms selecting suitable locations to operate warehousing facilities. The results highlight that transportation plays a prominent role in the location of warehouses with proximity to airport, highway infrastructure, and rail to a lesser extent. However, the proportion of industrial land use provides the largest role in the model suggesting that available land and zoning is the most important factor. This paper also studies the impact of warehouse location on freight transportation trips. The resulting analysis highlights that GPS derived trips arising from warehouses near a major airport in Toronto tend to travel 1.8 times further than trips pertaining to other warehouses in the region. This suggests that trips related to the airport are more likely to be connected to a larger supply chain process.     

Strategic design and operational management optimization of a multi stage physical distribution system

Design and management of logistic networks is one of the most critical issues in supply chain management. However, the literature does not contain any effective models, methods, and applications that simultaneously support management decisions in the strategic design of the distribution system, in the operational planning and organization of vehicles, and in container trips organization adopting different modes of transportation. The aim of this paper is to illustrate an original framework for the design and optimization of a multi echelon and multi level production/distribution system that combines mixed-integer linear programming modeling with cluster analysis, heuristic algorithms, and optimal transportation rules. A significant case study is illustrated revealing the effectiveness of the approach and tools proposed.     

Resilience of urban transportation systems. Concept,characteristics, and methods.

Urban transportation systems are susceptible to disturbances, interruptions, and risks from natural and human causes. The concept of resilience has been introduced into urban transportation systems to reduce the consequences of its disruptions. Recently, this concept has attracted considerable interest from researchers from different areas of transportation and mobility. However, several investigations about this issue were carried out under different perspectives, resulting in a more comprehensive concept, which includes adapting and transforming systems for different levels of equilibrium. This article provides a thorough and conceptual review of resilience applied to urban transportation systems focusing on its definitions, characteristics, and quantification methods. Based on this, the evaluation of the level of resilience of mobility subsystems is discussed to provide the basic framework for generating a tool to assess the resilience of the urban transportation system.     

Assessing the role of network topology in transportation network resilience

The abstract representation of a transportation system as a network of nodes and interconnecting links, whether that system involves roadways, railways, sea links, airspace, or intermodal combinations, defines a network topology. Among the most common in the context of transportation systems are the grid, ring, hub-and-spoke, complete, scale-free and small-world networks. This paper investigates the role of network topology, and the topology’s characteristics, in a transportation system’s ability to cope with disaster. Specifically, the paper hypothesizes that the topological attributes of a transportation system significantly affect its resilience to disaster events. Resilience accounts for not only the innate ability of the system to absorb externally induced changes, but also cost-effective and efficient, adaptive actions that can be taken to preserve or restore performance post-event. Comprehensive and systematically designed numerical experiments were conducted on 17 network structures with some relation to transportation system layout. Resilience of these network structures in terms of throughput, connectivity or compactness was quantified. Resilience is considered with and without the benefits of preparedness and recovery actions. The impact of component-level damage on system resilience is also investigated. A comprehensive, systematic analysis of results from these experiments provides a basis for the characterization of highly resilient network topologies and conversely identification of network attributes that might lead to poorly performing systems.     

Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey

This study examines transit mode choice preferences of New Jersey commuters traveling to New York City (NYC) with the objective of assisting trans-Hudson ferry service planning on the basis of variables significantly affecting mode choice. A stated preference survey was conducted to collect data from a total of 2134 respondents who commute from northern New Jersey to NYC. A mixed multinomial logit model (MMLM) with random coefficients was used to analyze the data. The study considered the choice between four transit modes—ferry, PATH train, bus, and commuter rail—all of which can be used to cross the Hudson River to travel from New Jersey to NYC. Essential model results are presented for all four modes, but detailed results are discussed for only the ferry, PATH, and bus modes. Model results indicate that commuters' choices are affected not only by travel time and cost for the segment of the journey that crosses the Hudson River, but also reliability and comfort during that segment, number of access modes required by the journey, and travel time and cost of trips from home to stations/terminals before crossing the river. The results also show that commuters' attachment to the modes they currently use serves as a barrier to switching to other modes. The most important takeaway from this research for transit service planning is that there is a need to consider the attributes of both individual trip segments as well as entire journeys between commuters' home and work when exploring changes to transit service characteristics.     

The path and time efficiency of residents' trips of different purposes with different travel modes: An empirical study in Guangzhou,China

Improving residents' travel efficiency and reducing carbon emissions from travel are the key issues for sustainable development of urban transportation. This study first employed a circuity index to measure the path efficiency of residents' trips based on 2015 survey data in Guangzhou and developed a generalized additive model (GAM) to investigate the relationship between the path efficiency and travel distance for different purposes of trip and different travel modes. On this basis, it further evaluated the time efficiency of different travel modes for each trip. The results showed that there is a complex and nonlinear relationship between the path efficiency and travel distance, which differs between different purposes of trips and different travel modes. In general, trips by non-motorized transport have a lower circuity index and higher path efficiency than those by cars or public transport. Moreover, non-motorized transport is the time-efficiency optimal mode for almost half of the trips, especially for daily shopping trips. However, people prefer to choose public transport on their trips even though public transport is not the time-efficiency optimal mode for these trips. Generally, only about half of the residents chose the time-efficiency optimal mode for their trips. Those who did not choose the time-efficiency optimal mode tended to choose the modes with higher carbon-intensity. The conclusions of this study indicate that for improving travel efficiency and reducing carbon emissions from transport, more efforts should be focused on the non-motorized travel environment and developing relevant policies to encourage more walking and cycling.     

Measuring non-motorized accessibility: issues,alternatives, and execution

While the transportation planning literature contains many examples of the calculation of measures of accessibility for urban areas, these measures are largely restricted to motorized modes and to a handful of destination activities. This paper explores the issues related to the development of accessibility measures for non-motorized modes, namely bicycling and walking. We note that difficulties in calculating accessibility measures arise primarily from problems with data quality, the zonal structure of transportation planning models, and the adequacy of models and travel networks for describing and predicting travel by non-motorized modes. We present practical strategies for addressing these issues. The application of these methods is illustrated with the calculation of accessibility measures for a small study area in Minneapolis, MN (USA). The paper concludes with some direction for future development of non-motorized accessibility measures and ideas about their applicability to the practice of transportation planning.     

Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS

As the practices of transportation engineering and planning evolve from “data poor” to “data rich”, methods to automate the translation of data to information become increasingly important. A major field of study is the automatic identification of travel modes from passively collected GPS data. In previous work, the authors have developed a robust modal classification system using an optimized combination of statistical inference techniques. One problem that remains very difficult is the correct identification of transit travel, particularly when the system is operating in mixed traffic. This type of operation generates a wide range of values for many travel parameters (average speed, maximum speed, and acceleration for example) which have similar characteristics to other urban modes. In this paper, we supplement the previous research to improve the identification of transit trips. The method employed evaluates the likelihood that GPS travel data belong to transit by comparing the location and pattern of zero-travel speeds (stopping) to the presence of transit stops and signalized intersections. These comparisons are done in a GIS. The consideration of the spatial attributes of GPS data vastly improves the accuracy of transit travel prediction.     

Conceptualising convenience: Transportation practices and perceptions of inner-urban high density residents in Brisbane,Australia

High-density living in inner-urban areas has been promoted to encourage the use of more sustainable modes of travel to reduce greenhouse gas emissions. However, previous research presents mixed results on the relationship between living in proximity to transport systems and reduced car-dependency. This research examines inner-city residents’ transportation practices and perceptions, via 24 qualitative interviews with residents from high-density dwellings in inner-city Brisbane, Australia. Whilst participants consider public transport accessible and convenient, car use continues to be relied on for many journeys. Transportation choices are justified through complex definitions of convenience containing both utilitarian and psycho-social elements, with three key themes identified: time-efficiency, single versus multi-modal trips, and distance to and purpose of journey, as well as attitudinal, affective and symbolic elements related to transport mode use. Understanding conceptions of transport convenience held by different segments of the transport users market, alongside other factors strongly implicated in travel mode choice, can ensure targeted improvements in sustainable transport service levels and infrastructure as well as information service provision and behavioural change campaigns.     

Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price

This study aimed to explore the impacts of COVID-19 on car and bus usage and their relationships with land use and land price. Large-scale trip data of car and bus usage in Daejeon, South Korea, were tested. We made a trip-chain-level data set to analyze travel behavior based on activity-based travel volumes. Hexagonal cells were used to capture geographical explanatory variables, and a mixed-effect regression model was adopted to determine the impacts of COVID-19. The modeling outcomes demonstrated behavioral differences associated with using cars and buses amid the pandemic. People responded to the pandemic by reducing their trips more intensively during the daytime and weekends. Moreover, they avoided crowded or shared spaces by reducing bus trips and trips toward commercial areas. In terms of social equity, trips of people living in wealthier areas decreased more than those of people living in lower-priced areas, especially trips by buses. The findings contribute to the previous literature by adding a fundamental reference for the different impacts of pandemics on two universal transportation modes.     

Improving rural accessibility by locating multimodal mobility hubs

In rural regions, public transportation is often characterized by low accessibility as well as long waiting and travel times. In order to improve rural transportation systems, public decision-makers intend to implement alternative on-demand mobility modes. Herein, new intermodal travel itineraries with transfers at multimodal mobility hubs may enable faster public connections and thereby strengthen public transportation. Against this background, we present a decision support tool for locating multimodal mobility hubs to improve intermodal accessibility. As objectives, we aim at maximizing accessibility to workplaces and to places of private need. Our model decides on locations of multimodal mobility hubs and on the available on-demand mobility modes offered in addition to existing public transportation. We develop our model in an agile process together with rural decision-makers in the district of Heinsberg, Germany, and apply it in a real-world case study. As input for our model, we account for the existing public transportation system, identify points-of-interest, and estimate commuting volumes to workplaces based on official commuting data. Results promise a high potential to improve accessibility in rural areas. However, most of the improvement stems from unimodal car sharing trips.     

Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco

Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models. This research focuses on understanding how docked bikesharing and dockless e-bikesharing models complement and compete with respect to user travel behaviors. To inform our analysis, we used two datasets from February 2018 of Ford GoBike (docked) and JUMP (dockless electric) bikesharing trips in San Francisco. We employed three methodological approaches: 1) travel behavior analysis, 2) discrete choice analysis with a destination choice model, and 3) geospatial suitability analysis based on the Spatial Temporal Economic Physiological Social (STEPS) to Transportation Equity framework. We found that dockless e-bikesharing trips were longer in distance and duration than docked trips. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. JUMP users were far less sensitive to estimated total elevation gain than were GoBike users, making trips with total elevation gain about three times larger than those of GoBike users, on average. The JUMP system achieved greater usage rates than GoBike, with 0.8 more daily trips per bike and 2.3 more miles traveled on each bike per day, on average. The destination choice model results suggest that JUMP users traveled to lower-density destinations, and GoBike users were largely traveling to dense employment areas. Bike rack density was a significant positive factor for JUMP users. The location of GoBike docking stations may attract users and/or be well-placed to the destination preferences of users. The STEPS-based bikeability analysis revealed opportunities for the expansion of both bikesharing systems in areas of the city where high-job density and bike facility availability converge with older resident populations.     

Measuring cycling accessibility in metropolitan areas

Different measures of cycling accessibility have been widely introduced in transportation planning. However, those measurements are mainly restricted by the availability of travel behavior data. In addition, there has been limited comprehensive research on the importance of cycling accessibility to destinations based on the travel time or distance. In this paper, a new index for measuring bikeability in metropolitan areas is introduced. A Cycling Accessibility Index (CAI) is developed for quantifying cycling accessibility within local areas in metropolitan Melbourne, Australia. CAI is defined according to gravity-based measures of accessibility. This index measures cycling accessibility levels in terms of diversity of different land uses, number of activities in statistical areas, and the travel impedance between origins and destinations. The Victorian Integrated Survey of Travel and Activity (VISTA) dataset was used to evaluate the index and investigate the association between the cycling accessibility levels and the number of bicycle trips in local areas. The index is assessed by investigating the association between levels of cycling accessibility and the number of bicycle trips in statistical areas. Key findings indicate that there is a significant positive association between bike trips and the CAI.     

Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.     

Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.