Urban logistics and spatial territorial intelligence indicators: State-of-the-art, typology and implications for Latin American cities

This paper reviews the state of the art in spatial accessibility and attractiveness indicators for urban freight transport and logistics, from a territorial intelligence and analytics viewpoint. It proposes a first typology of indicators and analyzes their potential in Latin American urban planning and development. After presenting the main notions of business intelligence and analytics, as well as a definition of territorial intelligence and analytics, the paper proposes an overview of territorial intelligence indicators, focusing on accessibility and attractiveness indicators, and a typology of five categories of indicators: infrastructure-based indicators, generation-based indexes, distance/time/cost measures, gravity-based indicators and space-time compatibility indexes. Finally, the main issues for implementing such indicators are presented, in terms of data requirements and potential applications focusing on the Latin American context.El presente artículo presenta el estado del arte sobre indicadores de accesibilidad y de atractividad espacial para el transporte de carga y la logística urbana, desde el punto de vista de la inteligencia y la analítica territorial. El artículo propone una primera tipología de indicadores, y analiza el potencial de su uso en la planeación y desarrollo urbano en Latinoamérica. Tras presentar las principales nociones de inteligencia y analítica de negocios, y proponer una definición de la inteligencia y analítica territorial, el artículo propone una visión de conjunto de los indicadores de inteligencia territorial, con un foco en los de accesibilidad y atractividad, y una tipología con cinco categorías de indicadores: de infraestructura, de generación, de distancia/tiempo/costo, gravitatorios y de compatibilidad espacio-temporal. Finalmente, se presentan las principales cuestiones en la implementación de dichos indicadores, en términos de requerimientos en datos y de potenciales aplicaciones, con un foco en el contexto latinoamericano

Time, the other dimension of urban form: Measuring the relationship between urban density and accessibility to grocery shops in the 10-minute city

Compact settlements take advantage of economies of scale by sustaining a system of high-quality socio-economic services at close proximities. Urban density with a balanced mix of uses also benefits walking and cycling as mobility modes that provide sufficient access to urban amenities, especially when combined with effective public transport. Indeed, walking and cycling can decrease the use of cars for short-distance trips. From this perspective, urban density can help to reduce pollution, optimise energy consumption and decrease infrastructural expenditures while contributing to more attractive urban environments. These ideas have induced a new wave of time geography planning concepts, such as the ‘10-minute city’, to enhance urban sustainability. For these concepts to move beyond visionary narratives, they must be expressed in specific empirical frameworks. Thus, the current research focuses on accessibility to grocery shops, as an essential urban service, in the Stavanger metropolitan area (Norway) using 10 minutes isochrones for walking and cycling. The study integrates open data, GIS network analyses, statistical regressions and bivariate representations of the results. The research estimates the level of serviceability by quantifying the number of shops that are accessible for each location and interrelates this estimation with spatial and population densities. The paper also presents a method to detect spatial inequalities by visualising over/under-serviced areas. This visualisation can become a tool to support strategies to rebalance such imbalances. Moreover, this study offers a practical approach towards the ‘10-minute city’ concept, as it can be adjusted to different isochrones at different spatial scales. In general, this approach can serve both to analyse existing contexts and to model strategies to support sustainability policies, such as urban densification and the promotion of environmental-friendly transport.publishedVersio

The Evaluation of E-commerce Efficiency in China using DEA-Tobit model: evidence from Taobao data

Using the analytical framework of DEA-Tobit, this paper investigates the efficiency of e-commerce in China\u27s provinces based on the cross-section data of 31 provinces in China and the data of e-commerce service providers from Taobao’s open platform. The data envelopment analysis (DEA) is used to calculate the technical efficiency and scale efficiency. Furthermore the paper gives an empirical test on the relationship between the scale efficiency and influencing factors by using the censored Tobit model. The results show there are significant regional differences in the efficiency of e-commerce services in provinces of China, and the Real GDP per capita, the seller number on e-commerce platform, the retail sales and wholesale are important reasons for the different efficiency in each province of China. This study provides a domain-specific, integrative approach in evaluating the E-commerce development combining macro data from National Bureau of Statistics of China and micro data from taobao.com

THE ECOLOGICAL IMPACT OF TRANSPORTATION INFRASTRUCTURE

Abstract There is a long-standing debate over whether new roads unavoidably lead to environmental damage, especially forest loss, but causal identification has been elusive. Using multiple causal identification strategies, we study the construction of new rural roads to over 100,000 villages and the upgrading of 10,000 kilometers of national highways in India. The new rural roads had precisely zero effect on local deforestation. In contrast, the highway upgrades caused substantial forest loss, which appears to be driven by increased timber demand along the transportation corridors. In terms of forests, last mile connectivity had a negligible environmental cost, while expansion of major corridors had important environmental impacts

Data-Driven Framework for Understanding & Modeling Ride-Sourcing Transportation Systems

Ride-sourcing transportation services offered by transportation network companies (TNCs) like Uber and Lyft are disrupting the transportation landscape. The growing demand on these services, along with their potential short and long-term impacts on the environment, society, and infrastructure emphasize the need to further understand the ride-sourcing system. There were no sufficient data to fully understand the system and integrate it within regional multimodal transportation frameworks. This can be attributed to commercial and competition reasons, given the technology-enabled and innovative nature of the system. Recently, in 2019, the City of Chicago the released an extensive and complete ride-sourcing trip-level data for all trips made within the city since November 1, 2018. The data comprises the trip ends (pick-up and drop-off locations), trip timestamps, trip length and duration, fare including tipping amounts, and whether the trip was authorized to be shared (pooled) with another passenger or not. Therefore, the main goal of this dissertation is to develop a comprehensive data-driven framework to understand and model the system using this data from Chicago, in a reproducible and transferable fashion. Using data fusion approach, sociodemographic, economic, parking supply, transit availability and accessibility, built environment and crime data are collected from open sources to develop this framework. The framework is predicated on three pillars of analytics: (1) explorative and descriptive analytics, (2) diagnostic analytics, and (3) predictive analytics. The dissertation research framework also provides a guide on the key spatial and behavioral explanatory variables shaping the utility of the mode, driving the demand, and governing the interdependencies between the demand’s willingness to share and surge price. Thus, the key findings can be readily challenged, verified, and utilized in different geographies. In the explorative and descriptive analytics, the ride-sourcing system’s spatial and temporal dimensions of the system are analyzed to achieve two objectives: (1) explore, reveal, and assess the significance of spatial effects, i.e., spatial dependence and heterogeneity, in the system behavior, and (2) develop a behavioral market segmentation and trend mining of the willingness to share. This is linked to the diagnostic analytics layer, as the revealed spatial effects motivates the adoption of spatial econometric models to analytically identify the ride-sourcing system determinants. Multiple linear regression (MLR) is used as a benchmark model against spatial error model (SEM), spatially lagged X (SLX) model, and geographically weighted regression (GWR) model. Two innovative modeling constructs are introduced deal with the ride-sourcing system’s spatial effects and multicollinearity: (1) Calibrated Spatially Lagged X Ridge Model (CSLXR) and Calibrated Geographically Weighted Ridge Regression (CGWRR) in the diagnostic analytics layer. The identified determinants in the diagnostic analytics layer are then fed into the predictive analytics one to develop an interpretable machine learning (ML) modeling framework. The system’s annual average weekday origin-destination (AAWD OD) flow is modeled using the following state-of-the-art ML models: (1) Multilayer Perceptron (MLP) Regression, (2) Support Vector Machines Regression (SVR), and (3) Tree-based ensemble learning methods, i.e., Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost). The innovative modeling construct of CGWRR developed in the diagnostic analytics is then validated in a predictive context and is found to outperform the state-of-the-art ML models in terms of testing score of 0.914, in comparison to 0.906 for XGBoost, 0.84 for RFR, 0.89 for SVR, and 0.86 for MLP. The CGWRR exhibits outperformance as well in terms of the root mean squared error (RMSE) and mean average error (MAE). The findings of this dissertation partially bridge the gap between the practice and the research on ride-sourcing transportation systems understanding and integration. The empirical findings made in the descriptive and explorative analytics can be further utilized by regional agencies to fill practice and policymaking gaps on regulating ride-sourcing services using corridor or cordon toll, optimally allocating standing areas to minimize deadheading, especially during off-peak periods, and promoting the ride-share willingness in disadvantage communities. The CGWRR provides a reliable modeling and simulation tool to researchers and practitioners to integrate the ride-sourcing system in multimodal transportation modeling frameworks, simulation testbed for testing long-range impacts of policies on ride-sourcing, like improved transit supply, congestions pricing, or increased parking rates, and to plan ahead for similar futuristic transportation modes, like the shared autonomous vehicles

Public survey instruments for business administration using social network analysis and big data

Purpose: The subject matter of this research is closely intertwined with the scientific discussion about the necessity of developing and implementing practice-oriented means of measuring social well-being taking into account the intensity of contacts between individuals. The aim of the research is to test the toolkit for analyzing social networks and to develop a research algorithm to identify sources of consolidation of public opinion and key agents of influence. The research methodology is based on postulates of sociology, graph theory, social network analysis and cluster analysis. Design/Methodology/Approach: The basis for the empirical research was provided by the data representing the reflection of social media users on the existing image of Russia and its activities in the Arctic, chosen as a model case. Findings: The algorithm allows to estimate the density and intensity of connections between actors, to trace the main channels of formation of public opinion and key agents of influence, to identify implicit patterns and trends, to relate information flows and events with current information causes and news stories for the subsequent formation of a "cleansed" image of the object under study and the key actors with whom this object is associated. Practical Implications: The work contributes to filling the existing gap in the scientific literature, caused by insufficient elaboration of the issues of applying the social network analysis to solve sociological problems. Originality/Value: The work contributes to filling the existing gap in the scientific literature formed as a result of insufficient development of practical issues of using analysis of social networks to solve sociological problems.peer-reviewe