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Considering and measuring the similarity of human activities remains challenging. Existing studies of similarity measures based on traditional edit distance (ED), specifically on activity patterns, do not reflect the spatiotemporal characteristics in the measurement model. Additionally, interdependence between activities is ignored in existing multidimensional sequence alignment methods. To address the gap, we initially extend the traditional edit distance to a space-time-weighted edit distance (STW-ED). Specifically, differences in distance and time between activities are considered cost functions in the operation cost calculation (insertion, deletion, and substitution). We advance STW-ED to an augmented space-time-weighted edit distance method (ASTW-ED) that integrates an optimum-trajectory-based multidimensional sequence alignment method (OT-MDSAM) with STW-ED, treating the nonspatiotemporal dimensions as augment factors. In addition, ontology is considered for the similarity measure for nonspatiotemporal dimensions. To show the feasibility of our proposed approach, we conduct an empirical study based on an activity-based travel survey in the Puget Sound Region. Eight clusters (homemakers, regular workers with a colorful life, regular workers with a monotonous life, part-time workers, recreation travelers, senior travelers, no-job travelers, and night owl adventurers) are identified based on ASTW-ED and ontology. To cluster the similarity matrix derived from the introduced methods, the affinity propagation (AP) clustering method is employed because it is free of prior knowledge for clustering and can produce exemplars of the clusters. The empirical study indicates that, relative to existing methods for multidimensional activity similarity measurement and clustering, ASTW-ED performs better in terms of within-group homogeneity and between-group heterogeneity of clusters. In addition, the results reveal that ontology can improve clustering performance if it is considered for nonspatiotemporal dimensions provide better understanding of human behavior for urban governance..

Ridesourcing, or on-demand ridesharing, is quickly changing today's travel. Recently, research has linked socio-demographics to ridesourcing use. However, little of the research has focused on the impacts of built environment, an important factor to consider in understanding travel behavior. This study applied Geographically Weighted Poisson Regression (GWPR) and examined the spatial relationships between built environment and ridesourcing demand. We used 2016–2017 ridesourcing trip data from a transportation network company (TNC), RideAustin, in Austin, Texas. By capturing the spatial heterogeneity, the GWPRs considerably improve modeling fit compared to the global models. Modeling results present strong relationships between ridesourcing demand and built environment variables (i.e., density, land use, infrastructure, and transit accessibility). More importantly, the results demonstrate significant spatial variations of the effects of both built environment and socio-economic variables and geographic trends from urban to suburban neighborhoods. Overall, these findings suggest that built environment factors have significant impacts on ridesourcing demand, and it is important to consider the spatial context. The study provides useful insights for understanding ridesourcing use as a function of built environment and have important implications for transportation planning, demand modeling, and urban governance.

Recent studies have suggested that rail transit not only facilitates urban growth but also promotes urban agglomeration. Yet research that links industrial agglomeration with rail transit is scant—what types of industries are likely to cluster near rail stations? To what extent can rail transit access be seen as having an influence on industrial agglomeration? And how do these interactions vary as rail transit proximity increases? To answer these and related questions, we investigate the relationship between industrial agglomeration and rail transit in the Dallas-Fort Worth metropolitan area using the Longitudinal Employer Dynamics (LEHD) employment data from 2014 at the census block level. First, we use the Local Indicator of Spatial Association statistics (LISA) tests to identify industrial agglomeration patterns within the study area. We then use logistics models to reveal the relationship between rail transit proximity and industrial agglomeration. Our study finds that the impacts of rail transit on industrial agglomeration, in terms of magnitudes and signs, are mixed across industries. The varying results suggest that the benefits of rail transit access exhibit considerable demand from certain industry sectors including the manufacturing, knowledge, and services industries, while exerting weaker forces in pulling agglomeration in its immediate environs among other industries (including the retail trade sector). In practice, these results are useful for justifying evidence-based rail transit planning.

Natural disasters, intentional attacks, and operational incidents are posing increasing threats on rail transit network. The vulnerability of rail transit network becomes an important concern of researchers and rail managers. This paper proposes a station-based accessibility approach addressing passenger flow and land use characteristics in rail transit network vulnerability analysis. Land use variables are measured as the independency degree on rail transit. The reduction ratio of network accessibility before and after incidents is calculated to measure the potential consequences. Based on results of comparisons with existing methods with the help of an example problem, the proposed accessibility measure demonstrates better and more reasonable results as not only the rail network and passenger flow but also the land use and travel alternative variables interacting with rail transit are accounted. The proposed method is then applied to Shanghai metro network. The data for analysis include rail transit network data, passenger flow data, and land use data around stations. Results indicate that the vulnerability of rail transit network is jointly affected by its network topology, passenger flow, and land use variables. Unbalanced land use, high plot ratio, and the less travel alternatives will increase the dependency of land on rail transit travel, leading to high network vulnerability once disrupted. Results of this work will inform rail transit managers of the degree of network vulnerability and critical stations and links as well as the land use dependency on vulnerability. Findings of this study may have implications not only for the planning of other transit modes to enhance the resilience of public transit network in vulnerable areas but also for the land use development around rail stations.

Increased attention has been paid to travel behaviour in circumstances of extreme weather conditions that are expected with climate change, and the analyses usually address intra-city travel. There is lack of assessments on inter-city travel which has less redundancy and is more exposed to extreme weather threats. In addition, much of the research has been carried out in developed countries. This paper provides new perspectives by investigating how people adapt their inter-city travel behaviour to flooding impacts in Bangladesh. With an orthogonal design of three flooding scenarios, questionnaire data were collected in 14 coastal and inland areas. Results of the statistical analyses identify the significant impacts of flooding on people’s inter-city travel and reveal significant differences in attitudes and responses to flooding and extreme weather in coastal compared to inland locations. The main factors significantly affecting travel behaviour choice are road disruption, isolation by flood water, and flood frequency. These factors are felt differently in coastal and inland locations. The most common responses are cancelling trips or changing destinations. It is recommended that when making flooding adaptation decisions, it is important to protect road infrastructure and guarantee accessible routes in coastal areas, while offering more flood adaptation education to the inland people.

In this paper, we proposed an improved transportation network efficiency (TNE) measurement method, which is called passenger- and speed-weighted efficiency (PSWE). The new method is used to measure both origin-destination-level (OD-level) transportation efficiency and citywide transportation efficiency by integrating with the excess commuting (EC) framework. To show the feasibility of the proposed measurement method, we chose New York City (NYC) as the study area and employed open taxi data. By calculating the efficiency value of the OD-level, we discovered that the average distance of highly efficient taxi trips is much longer than that of inefficient trips. Then, the OD-level efficiency value is applied to the EC framework by replacing the conventional cost unit (time or distance) with the proposed efficiency impedance (Eimp). The maps of minimized OD flows indicate that taxi trips within Manhattan and to the two airports (LGA and JFK) should be particularly considered if citywide efficiency is expected to be improved.

This paper reports on LandSys, a land use model that integrates a Cellular Automata (CA) model and agent-based models to facilitate transportation demand modeling and analysis. The LandSys model simulates the spatial suitability of land use change over time based on both the land use suitability and the impacts of neighboring land use types in a CA model framework, coupled with individual decision-makers’ behaviors with agent-based models (e.g., household, employment, and developer agents). To generate inputs for transportation models, two equilibriums in the land use market are considered: land development equilibrium simulates land use change at a manageable cell level (50 m × 50 m), while land supply–demand equilibrium allocates firms and agents based on the bid-rent theory. Data from Orange County, FL, are used in a case study for model estimation and validation. A comparison of model results with actual data from 2000 shows that the proposed LandSys model is effective in estimating land use change, with an accuracy of 85.4%. The model is also responsive to land use policies and could be used for what–if analysis.