The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City: Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goal is figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the Detroit, exploring the potential correlation between these diverse data features, and determining whether each type of data (e.g., traffic volume data) could be a useful factor in the confirmed-cases prediction. In addition, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next one week. The model demonstrated a promising prediction result with a coefficient of determination (R^2) of up to approximately 0.91. Moreover, in order to provide statistical evaluation measures of confirmed-case prediction and to quantify the prediction effectiveness of each type of data, the prediction results of six feature groups are presented and analyzed. Furthermore, six essential observations with supporting evidence and analyses are presented. The goal of this paper is to present a proposed approach which can be applied, customised, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.
This paper continues to highlight trends in mobility and sociability in New York City (NYC), and supplements them with similar data from Seattle, WA, two of the cities most affected by COVID-19 in the U.S. Seattle may be further along in its recovery from the pandemic and ensuing lockdown than NYC, and may offer some insights into how travel patterns change. Finally, some preliminary findings from cities in China are discussed, two months following the lifting of their lockdowns, to offer a glimpse further into the future of recovery.
Using a unique data set containing about 15.06 million truck transportation records in five months, we investigate the highway freight transportation diversity of 338 Chinese cities based on the truck transportation probability $p_{ij}$ from one city to the other. The transportation probabilities are calculated from the radiation model based on the geographic distance and its cost-based version based on the driving distance as the proxy of cost. For each model, we consider both the population and the gross domestic product, and find quantitatively very similar results. We find that the transpo…
We investigate, through a data-driven contact tracing model, the transmission of COVID-19 inside buses during distinct phases of the pandemic in a large Brazilian city. From this microscopic approach, we recover the networks of close contacts within consecutive time windows. A longitudinal comparison is then performed by upscaling the traced contacts with the transmission computed from a mean-field compartmental model for the entire city. Our results show that the effective reproduction numbers inside the buses, $Re^{bus}$, and in the city, $Re^{city}$, followed a compatible behavior during th…
Measuring the interaction between cities is an important research topic in many disciplines, such as sociology, geography, economics and transportation science. The traditional and most widely used spatial interaction model is the gravity model, but it requires the parameters to be artificially set. In this paper, we propose a parameter-free interactive city choice (ICC) model that measures intercity interaction from the perspective of individual choice behavior. The ICC model assumes that the probability of an individual choosing to interact with a city is proportional to the number of opport…
This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from taxis. The granularity of the GPS data necessary for this analysis is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. This method is applied to a dataset of nearly 700 million taxi trips in New York City, which i…
University students' day-to-day lives largely depend on transportation. Public transit alternatives are not widely available in the United States' small-town, rural, and suburban collegiate environments (U.S.). This study disseminated an online survey among international students studying at The University of Alabama (UA) campus, a predominantly white institution in Tuscaloosa, Alabama. The objective of this research was to investigate and analyze international student travel experiences in a city with a significant college-affiliated population and highlight particular transportation issues i…
Based on the SEIR model and the modeling of urban transportation networks, a general-purpose simulator for the spread of epidemics in Chinese cities is built. The Chinese public transportation system between over 340 prefectural-level cities is modeled as a multi-layer bi-partite network, with layers representing different means of transportation (airlines, railways, sail routes and buses), and nodes divided into two categories (central cities, peripheral cities). At each city, an open-system SEIR model tracks the local spread of the disease, with population in- and out-flow exchanging with th…
We perform a systematic analysis on the large-scale taxi trip data to uncover urban mobility and city dynamics in multimodal urban transportation environments. As a case study, we use the taxi origin-destination trip data and some additional data sources in Washington DC area. We first study basic characteristics of taxi trips, then focus on five important aspects. Three of them concern urban mobility, which are respectively mobility and cost including effect of traffic congestion, trip safety, and multimodal connectivity; the other two pertain to city dynamics, which are respectively transpor…
Urban mobility increasingly relies on multimodality, combining the use of bicycle paths, streets, and rail networks. These different modes of transportation are well described by multiplex networks. Here we propose the overlap census method which extracts a multimodal profile from a city's multiplex transportation network. We apply this method to 15 cities, identify clusters of cities with similar profiles, and link this feature to the level of sustainable mobility of each cluster. Our work highlights the importance of evaluating all the transportation systems of a city together to adequately …
This study models and examines commuter's preferences for short-distance transportation modes, namely: walking, taking a bus or riding a metro. It is used a unique dataset from a large-scale field experiment in Singapore that provides rich information about tens of thousands of commuters' behavior. In contrast to the standard approach, this work does not relay on survey data. Conversely, the chosen transportation modes are identified by processing raw data (latitude, longitude, timestamp). The approach of this work exploits the information generated by the smart transportation system in the ci…
Towards a prioritised use of transportation infrastructures: the case of vehicle-specific dynamic access restrictions to city centres
CETINIA, Univ. Rey Juan Carlos（CETINIA，胡安·卡洛斯国王大学）；Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València（瓦伦西亚人工智能研究所 (VRAIN)，瓦伦西亚理工大学）；BISITE Research Group, University of Salamanca（BISITE研究组，萨拉曼卡大学）
Sharing rides could drastically improve the efficiency of car and taxi transportation. Unleashing such potential, however, requires understanding how urban parameters affect the fraction of individual trips that can be shared, a quantity that we call shareability. Using data on millions of taxi trips in New York City, San Francisco, Singapore, and Vienna, we compute the shareability curves for each city, and find that a natural rescaling collapses them onto a single, universal curve. We explain this scaling law theoretically with a simple model that predicts the potential for ride sharing in a…
In this work we analyze the architecture of real urban mobility networks from the multiplex perspective. In particular, based on empirical data about the mobility patterns in the cities of Bogotá and Medell\'ın, each city is represented by six multiplex networks, each one representing the origin-destination trips performed by a subset of the population corresponding to a particular socioeconomic status. The nodes of each multiplex are the different urban locations whereas links represent the existence of a trip from one node (origin) to another (destination). On the other hand, the different l…
The Citi Bike deployment changes the landscape of urban mobility in New York City and provides an example of a scalable solution that many other large cities are already adopting around the world. Urban stakeholders who are considering a similar deployment would largely benefit from a quantitative assessment of the impact of bike sharing on urban transportation, as well as associated economic, social and environmental implications. While the Citi Bike usage data is publicly available, the main challenge of such an assessment is to provide an adequate baseline scenario of what would have happen…
The goals of this paper are to present criteria, that allow to a priori quantify the attack stability of real world correlated networks of finite size and to check how these criteria correspond to analytic results available for infinite uncorrelated networks. As a case study, we consider public transportation networks (PTN) of several major cities of the world. To analyze their resilience against attacks either the network nodes or edges are removed in specific sequences (attack scenarios). During each scenario the size S(c) of the largest remaining network component is observed as function of…
Public urban mobility systems are composed by several transportation modes connected together. Most studies in urban mobility and planning often ignore the multi-layer nature of transportation systems considering only aggregated versions of this complex scenario. In this work we present a model for the representation of the transportation system of an entire city as a multiplex network. Using two different perspectives, one in which each line is a layer and one in which lines of the same transportation mode are grouped together, we study the interconnected structure of 9 different cities in Eu…
We propose a risk measure and construct an infectious diseases hazard map for India. Given an outbreak location, a hazard index is assigned to each city using an effective distance that depends on inter-city mobilities instead of geographical distance. We demonstrate its utility using an SIR model augmented with air, rail, and road data between top 446 cities. Simulations show that the effective distance from outbreak location reliably predicts the time of arrival of infection in other cities. The hazard index predictions compare well with the observed spread of SARS-CoV-2. The hazard map can …
Models focusing on interactions between land-use and transportation mostly assume an exogenous provision of transportation infrastructures. We investigate here co-evolutionary processes between land-use and transportation, at the scale of Mega-City Regions, by introducing a toy model of corresponding processes. In particular, our model is specifically tailored to include governance processes ruling the growth of transportation infrastructure. We show through stylised numerical simulations the potentialities of our model to reproduce a variety of dynamics when co-evolution is taken into account…
Intelligent transportation systems (ITSs) and other smart-city technologies are increasingly advancing in capability and complexity. While simulation environments continue to improve, their fidelity and ease of use can quickly degrade as newer systems become increasingly complex. To remedy this, we propose a hardware- and software-based traffic management system testbed as part of a larger smart-city testbed. It comprises a network of connected vehicles, a network of intersection controllers, a variety of control services, and data analytics services. The main goal of our testbed is to provide…
Surface transportation systems are an essential part of urban transportation infrastructure and are susceptible to damage from earthquakes. This damage, along with the lack of prior warning of earthquake events, may lead to severe and unexpected disruption of normal traffic patterns, which may seriously impair post-disaster response. Accordingly, it is important to understand and model the performance of urban transportation systems immediately following an earthquake, to evaluate its capability to support emergency response, e.g., the movement of firefighters, search and rescue teams and medi…
This work analyses methods for the identification and the stability under perturbation of a territorial community structure with specific reference to transportation networks. We considered networks of commuters for a city and an insular region. In both cases, we have studied the distribution of commuters' trips (i.e., home-to-work trips and viceversa). The identification and stability of the communities' cores are linked to the land-use distribution within the zone system, and therefore their proper definition may be useful to transport planners.
Designing and maintaining resilient transportation systems rely on identifying potential vulnerabilities and inefficiencies before crises occur. However, given the complexity of transportation networks, as well as the diversity of ways in which systems can fail, the problem of assessing the impacts of exceptional phenomena still lacks tools and metrics for analysis. Therefore, this paper aims to present a method for assessing the behavior of road networks during crises in which segments are disabled or unusable for a time. The method proposed was structured around two metrics calculated from t…
We investigate the traffic flows of the Korean highway system, which contains both public and private transportation information. We find that the traffic flow T(ij) between city i and j forms a gravity model, the metaphor of physical gravity as described in Newton's law of gravity, P(i)P(j)/r(ij)^2, where P(i) represents the population of city i and r(ij) the distance between cities i and j. It is also shown that the highway network has a heavy tail even though the road network is a rather uniform and homogeneous one. Compared to the highway network, air and public ground transportation estab…
In this paper, we introduce and test our algorithm to create a road network representation for city-scale active transportation simulation models. The algorithm relies on open and universal data to ensure applicability for different cities around the world. In addition to the major roads, their geometries and the road attributes typically used in transport modelling (e.g., speed limit, number of lanes, permitted travel modes), the algorithm also captures minor roads usually favoured by pedestrians and cyclists, along with road attributes such as bicycle-specific infrastructure, traffic signals…
Street networks may be planned according to clear organizing principles or they may evolve organically through accretion, but their configurations and orientations help define a city's spatial logic and order. Measures of entropy reveal a city's streets' order and disorder. Past studies have explored individual cases of orientation and entropy, but little is known about broader patterns and trends worldwide. This study examines street network orientation, configuration, and entropy in 100 cities around the world using OpenStreetMap data and OSMnx. It measures the entropy of street bearings in …
We study the influence that population density and the road network have on each others' growth and evolution. We use a simple model of formation and evolution of city roads which reproduces the most important empirical features of street networks in cities. Within this framework, we explicitely introduce the topology of the road network and analyze how it evolves and interact with the evolution of population density. We show that accessibility issues -pushing individuals to get closer to high centrality nodes- lead to high density regions and the appearance of densely populated centers. In pa…
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.
Advances in public transit modeling and smart card technologies can reveal detailed contact patterns of passengers. A natural way to represent such contact patterns is in the form of networks. In this paper we utilize known contact patterns from a public transit assignment model in a major metropolitan city, and propose the development of two novel network structures, each of which elucidate certain aspects of passenger travel behavior. We first propose the development of a transfer network, which can reveal passenger groups that travel together on a given day. Second, we propose the developme…
Transportation contributes a substantial fraction of all greenhouse gas emissions. One approach for reducing such emissions is to modify vehicles' route choices to minimize their fuel consumption or emission, which is known as eco-routing. Most eco-routing is based on vehicles choosing routes that minimize their individual fuel consumption or emissions. The Braess paradox demonstrates that when vehicles choose routes to minimize their individual goals, the aggregate effect may paradoxically result in the opposite net effect due to changes in congestion patterns. We construct a multiclass user …
OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilien…
Planning scholars have identified economic, safety, and social benefits of converting one-way streets to two-way. Less is known about how conversions could impact vehicular distances traveled - of growing relevance in an era of fleet automation, electrification, and ride-hailing. We simulate such a conversion in San Francisco, California. We find that its current street network's average intra-city trip is about 1.7% longer than it would be with all two-way streets, corresponding to 27 million kilometers of annual surplus travel. As transportation technologies evolve, planners must consider di…
We review and introduce a generalized reaction-diffusion approach to epidemic spreading in a metapopulation modeled as a complex network. The metapopulation consists of susceptible and infected individuals that are grouped in subpopulations symbolising cities and villages that are coupled by human travel in a transportation network. By analytic methods and numerical simulations we calculate the fraction of infected people in the metaopoluation in the long time limit, as well as the relevant parameters characterising the epidemic threshold that separates an epidemic from a non-epidemic phase. W…
The capability of traffic-information systems to sense the movement of millions of users and offer trip plans through mobile phones has enabled a new way of optimizing city traffic dynamics, turning transportation big data into insights and actions in a closed-loop and evaluating this approach in the real world. Existing research has applied dynamic Bayesian networks and deep neural networks to make traffic predictions from floating car data, utilized dynamic programming and simulation approaches to identify how people normally travel with dynamic traffic assignment for policy research, and in…
In 2020, the COVID-19 pandemic spreads all over the world. In order to alleviate the spread of the epidemic, various blockade policies have been implemented in many areas. In order to formulate a better epidemic prevention policy for urban energy consumption of the commercial tourism cities, this paper first analyses the energy characteristics of Macao during the epidemic period from two aspects, based on the energy consumption data of Macao. On this basis, the power consumption characteristics of commercial tourism cities during the epidemic were analyzed. Then, this paper provides analysis o…
Rapid urbanization places increasing stress on already burdened transportation systems, resulting in delays and poor levels of service. Billions of spatiotemporal call detail records (CDRs) collected from mobile devices create new opportunities to quantify and solve these problems. However, there is a need for tools to map new data onto existing transportation infrastructure. In this work, we propose a system that leverages this data to identify patterns in road usage. First, we develop an algorithm to mine billions of calls and learn location transition probabilities of callers. These transit…
Cell phone technology generates massive amounts of data. Although this data has been gathered for billing and logging purposes, today it has a much higher value, because its volume makes it very useful for big data analyses. In this project, we analyze the viability of using cell phone records to lower the cost of urban and transportation planning, in particular, to find out how people travel in a specific city (in this case, Buenos Aires, in Argentina). We use anonymized cell phone data to estimate the distribution of the population in the city using different periods of time. We compare thos…
New England Complex Systems Institute（新英格兰复杂系统研究所）
Measuring public sentiment is a key task for researchers and policymakers alike. The explosion of available social media data allows for a more time-sensitive and geographically specific analysis than ever before. In this paper we analyze data from the micro-blogging site Twitter and generate a sentiment map of New York City. We develop a classifier specifically tuned for 140-character Twitter messages, or tweets, using key words, phrases and emoticons to determine the mood of each tweet. This method, combined with geotagging provided by users, enables us to gauge public sentiment on extremely…
