Sustainable Urban Mobility: (A)Synchrony between Subway Systems

The present study analyses the differences between the subways systems of two cities, Lisbon (Portugal) and Brasília (Brazil), verifying the extent to which their subway systems are spatially integrated with the respective urban fabric-which would promote a better synchrony in terms of economy and sustainability. For interpreting the data, the methodology and tools of the Theory of the Social Logic of Space were used, by means of axial maps, researching the relationship between the constructed and empty (public spaces) areas of the urban structures. Based on the findings, meaningful differences between the subway systems and the configuration of urban spaces were observed, as a product of specific design matrixes. In Lisbon, the mobility associated to the subway seems to be encouraged by the integration of the system with the potential for movement provided by the urban tissue (making the secondary centralities articulated with the subway stations more dynamic). In Brasilia, however, there are several difficulties for such mobility, due to the predominant role of the empty spaces in the city which weakens the gathering potential of such areas, despite the fact that such articulation with secondary centralities is also present there.

Applying the Hidden Markov Model to Analyze Urban Mobility Patterns: An Interdisciplinary Approach

With the emergence of the Internet of Things(IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model(HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people’s eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activitytravel patterns of working adults in Hong Kong, two distinctive groups of balanced(38.4%) and work-oriented(61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people’s behavior in urban mobility research.

LiMo 2040 Sustainable urban Living and Mobility Concept-Elevator System

How can individual mobility in urban areas be maintained alongside scooters and cargo bikes if conventional vehicles are foreseeably no longer allowed to enter city centers?And how can urban living be combined with individual mobility in a sustainable and socially acceptable way?LiMo-2040 attempts to provide answers to these questions.It follows a holistic approach according to the criteria:As light,as compact and as simple(cost-effective)as possible.Modular e-vehicle concepts(consisting of vehicle cabin and chassis)are known,but have not yet been thought through to their logical conclusion.The LiMo cabin is not only a vehicle cabin,but also a component of a modern high-rise apartment.It therefore requires no parking space and combines urban living and individual mobility sustainably and cost-effectively.If a vehicle is needed,an app can be used to book a chassis that comes along autonomously and waits until the cabin,including its occupants,travels down a sophisticated rail system,docks and autonomously heads for the desired destination.

Exploring the Promises and Challenges of Urban Air Mobility (UAM)

This paper is focused on a higher-level report of a new generation of Unmanned Aerial Vehicle (UAV) technologies. Starting from the structural scalability of civil tiltrotors, design strategy and requirements for UAVs, and advanced composite materials, the increased speed and productivity requirements for tiltrotors have spawned several investigations associated with proprotor aero elastic stability augmentation and aerodynamic performance enhancements. The research emphasized the Large Civil Tilt Rotor as the configuration with the best potential to meet the technology goals, and the design, including the challenges of the Large Civil Tilt Rotor (LCTR). The design presented was economically competitive, with the potential for substantial impact on the air transportation system. The research includes some manufacturers of helicopters, drones and tiltrotors carrying out design studies and production of prototypes, as well as research projects aimed at designing, manufacturing, qualifying, and flight-testing the new wing of the Next-Generation Civil Tiltrotor Technology Demonstrator. Promises of Vertical Take-off and Landing (VTOL) aircraft, UAVs, Digitalization of Urban Air Mobility (UAM), and the “U-space” concept are discussed in the paper. The eight SUMP principles and possibilities of future advancements are emphasized.

Reconstructing urban wind flows for urban air mobility using reduced-order data assimilation

Advancements in uncrewed aircrafts and communications technologies have led to a wave of interest and investment in unmanned aircraft systems(UASs)and urban air mobility(UAM)vehicles over the past decade.To support this emerging aviation application,concepts for UAS/UAM traffic management(UTM)systems have been explored.Accurately characterizing and predicting the microscale weather conditions,winds in particular,will be critical to safe and efficient operations of the small UASs/UAM aircrafts within the UTM.This study implements a reduced order data assimilation approach to reduce discrepancies between the predicted urban wind speed with computational fluid dynamics(CFD)Reynolds-averaged Navier Stokes(RANS)model with real-world,limited and sparse observations.The developed data assimilation system is UrbanDA.These observations are simulated using a large eddy simulation(LES).The data assimilation approach is based on the time-independent variational framework and uses space reduction to reduce the memory cost of the process.This approach leads to error reduction throughout the simulated domain and the reconstructed field is different than the initial guess by ingesting wind speeds at sensor locations and hence taking into account flow unsteadiness in a time when only the mean flow quantities are resolved.Different locations where wind sensors can be installed are discussed in terms of their impact on the resulting wind field.It is shown that near-wall locations,near turbulence generation areas with high wind speeds have the highest impact.Approximating the model error with its principal mode provides a better agreement with the truth and the hazardous areas for UAS navigation increases by more than 10%as wind hazards resulting from buildings wakes are better simulated through this process.

基于Kriging遗传算法的高速公路应急车道管控优化

针对如何在不同交通流状况下有效提高高速公路运行效率和降低安全风险的问题,提出基于Kriging代理模型的遗传算法优化应急车道管控策略。结合应急车道开放策略的时空特性设计数学优化模型,通过引入Kriging代理模型,结合遗传算法搭建优化框架,采用仿真软件获取数据训练代理模型,以此求解带有开放时间和开放空间双重约束的总行程时间与总碰撞暴露时间最小化问题。对车道控制时间与空间变量的变化频次进行了约束,并对目标函数中效率与安全指标权重变化对优化结果的影响进行了分析。实验表明:该优化方法使路网总行程时间减小14.9%,碰撞暴露时间减小44.2%,控制效果提升。

Riding towards a sustainable future:an evaluation of bike sharing’s environmental benefits in Xiamen Island,China

In the pursuit of sustainable urbanization,Bike-Sharing Services(BSS)emerge as a pivotal instrument for promoting green,low-carbon transit.While BSS is often commended for its environmental benefits,we offer a more nuanced analysis that elucidates previously neglected aspects.Through the Dominant Travel Distance Model(DTDM),we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance.Utilizing multiscale geographically weighted regression(MGWR),we illuminate the relationship between BSS’s environmental benefits and built-environment attributes.The life cycle analysis(LCA)quantifies greenhouse gas(GHG)emissions from production to operation,providing a deeper understanding of BSS’s environmental benefits.Notably,our study focuses on Xiamen Island,a Chinese“Type Ⅱ large-sized city”(1–3 million population),contrasting with the predominantly studied“super large-sized cities”(over 10 million population).Our findings highlight:(1)A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO_(2)-eq,accumulating monthly savings of 144.477 t CO_(2)-eq.(2)Areas in the southwest,northeast,and southeast of Xiamen Island,characterized by high population densities,register significant BSS environmental benefits.(3)At a global level,the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation.(4)Regionally,MGWR enhances model precision,indicating that these five factors function at diverse spatial scales,affecting BSS’s environmental benefits variably.

Modeling urban scale human mobility through big data analysis and machinelearning

In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.

A demand forecasting model for urban air mobility in Chengdu,China

The successful application of new technologies such as remotely piloted aircraft systems,distributed electric propulsion systems,and automatic control systems on electric vertical take-off and landing(eVTOL)aircraft has prompted Urban Air Mobility(UAM)to be mentioned frequently.UAM is a newly raised transport mode of using eVTOL aircraft to transport people and cargo in urban areas,which is thought to share some of the traffic on the ground.One of the prerequisites for UAM to operate on a regular basis is that its demand can support the operating costs,so forecasting UAM demand is necessary.We conduct UAM demand forecasting based on the four-step method,focusing on improving the third-step modal split,and propose a demand forecasting model based on the logit model.The model combines a nested logit(NL)model with a multinomial logit(MNL)model to solve the problem of non-existent UAM sharing rates.We use Chengdu,China as an example,and focus on forecasting the UAM traffic demand in 2030 with the help of the four-step method.The results show that UAM is suitable for shared operation during the early stages.With a fully shared operation,the UAM share rate increases by 0.73%for every kilometer increase in distance.Moreover,UAM is more competitive than other modes for delivery distances exceeding 15 km.Finally,using the distributions of the share rate and traffic flow pattern from the simulation,we propose the routes that can be prioritized for UAM operations in Chengdu.

Overall eVTOL aircraft design for urban air mobility

Electric vertical takeoff and landing(eVTOL)aircraft have emerged as a potential alternative to the existing transportation system,offering a transition from two-dimensional commuting and logistics to three-dimensional mobility.As a groundbreaking innovation in both the automotive and aviation sectors,eVTOL holds significant promise but also presents notable challenges.This paper aims to address the overall aircraft design(OAD)approach specifically tailored for eVTOL in the context of Urban Air Mobility(UAM).In contrast to traditional OAD methods,this study introduces and integrates disciplinary methodologies specifically catered to eVTOL aircraft design.A case study is conducted on a tilt-duct eVTOL aircraft with a typical UAM mission,and the disciplinary performance,including initial sizing,aerodynamics,electric propulsion systems,stability and control,weight,mission analysis and noise,is examined using the OAD methodologies.The findings demonstrate that the current approach effectively evaluates the fundamental aircraft-level performance of eVTOL,albeit further high-fidelity disciplinary analysis and optimization methods are required for future MDO-based eVTOL overall aircraft design.

Remote Sensing Applied to NDVI and LST in Urban Streets in Southern Brazil

Urban evolution becomes a space-transforming agent capable of changing Normalised Difference Vegetation Index(NDVI)and Land Surface Temperature(LST)emissivity reflectance on the land surface.Therefore,this study identifies NDVI and LST variations locally,but it can also be applied in other cities on a global scale.In general,it aims to analyse NDVI and LST on surface areas of avenues and urban streets of Sector 1,located in downtown Passo Fundo,in the southern region of Brazil,from 2013 to 2020.Methodologically,treated Landsat 8 satellite images were used to obtain NDVI and LST variations in this study.Subsequently,data of 80 points in the avenues and streets of Sector 1 were collected over a distance of 100 meters spaced from each point.These data were statistically analysed concerning the dispersion of NDVI and LST from 2013 to 2020.The results revealed a strong interaction between NDVI and LST,demonstrating the need to conserve urban vegetation to improve temperature,which positively favours urban ambience indexes.

Is the Right to Mobility a Right to the City? Examining a Well-Accepted Planning Paradigm

In many countries mobility is one of the pillars of public policies for urban development.The“right to mobility”has been assimilated to the very concept of the“right to the city”put forward by Henri Lefebvre a half century ago.However,based on recent surveys,the present paper intends to show that a greater offer of mobility can have the opposite effect of increasing the economic dependence of poor peripheries,thus contributing to the phenomenon of socio-spatial segregation.To show this perverse effect of mobility,we use the results drawn out from case studies located in the Metropolitan Region of Rio de Janeiro,Brazil.In this context,the areas with the greatest mobility offer correspond to those that are now more dependent and emptied of employment and daily urban life.This greater accessibility has the effect of transforming these better served areas into dormitory peripheries.In contrast,other areas,with much less mobility offer,are able to avoid this direct competition with the city center.In conclusion,the paper proposes a necessary revision of the concept of mobility as a foundation for urban development which became even more pressing under the current COVID-19 crisis.

Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features:Case studies on two Brazilian urban centers

The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior.This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers,R0,and incidence by planning areas(PAs),as well as their correlations with the Human Development Index(HDI)and the number of trips.We analyzed dengue epidemics in 2002 at two Brazilian urban centers,Belo Horizonte(BH)and Rio de Janeiro(RJ),using PAs as spatial units.Our results reveal heterogeneous spatial scenarios for both cities,with very weak correlations between R0 and both the number of trips and the HDI;in BH,the values of R0 show a less spatial heterogeneous pattern than in RJ.For BH,there are moderate correlations between incidence and both the number of trips and the HDI;meanwhile,they weakly correlate for RJ.Finally,the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.

A reinforcement learning approach to vehicle coordination for structured advanced air mobility

Advanced Air Mobility(AAM)has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation.Its core objective is to provide highly automated air transportation services for passengers or cargo,operating at low altitudes within urban,suburban,and rural regions.AAM seeks to enhance the efficiency and environmental viability of the aviation sector by revolutionizing the way air travel is conducted.In a complex aviation environment,traffic management and control are essential technologies for safe and effective AAM operations.One of the most difficult obstacles in the envisioned AAM systems is vehicle coordination at merging points and intersections.The escalating demand for air mobility services,particularly within urban areas,poses significant complexities to the execution of such missions.In this study,we propose a novel multi-agent reinforcement learning(MARL)approach to efficiently manage high-density AAM operations in structured airspace.Our approach provides effective guidance to AAM vehicles,ensuring conflict avoidance,mitigating traffic congestion,reducing travel time,and maintaining safe separation.Specifically,intelligent learning-based algorithms are developed to provide speed guidance for each AAM vehicle,ensuring secure merging into air corridors and safe passage through intersections.To validate the effectiveness of our proposed model,we conduct training and evaluation using BlueSky,an open-source air traffic control simulation environment.Through the simulation of thousands of aircraft and the integration of real-world data,our study demonstrates the promising potential of MARL in enabling safe and efficient AAM operations.The simulation results validate the efficacy of our approach and its ability to achieve the desired outcomes.

River Network and the Risk of Flooding for the Railway and Subway Systems in the City of Sao Paulo, Brazil

This study investigated the risk of flooding along the Sao Paulo city＇s railway network and showed its latest technologies. To that end, we took into account topographic and hydrological features, as well as possible interferences of the flooding phenomenon with the optimal operation of the railway system. When preparing the maps and the flooding risk analysis, we used data from project SRTM （Shuttle Radar Topography Mission） integrated and processed in a GIS （geographical information system）, through version 2.14 of the geoprocessing software ＂QGis＂. Results showed risk exposure for logistics operations of the railway system for the transportation of passengers, especially in the Central and Eastern regions of the city, where there is a higher concentration of points of flooding, thus making it more difficult for passengers to come and go in the greatest city of Brazil.

Simulations of Carbon Dioxide Emissions Related to the Use of Public Transport for Students in a Higher Education Institution in Southern Brazil

Carbon dioxide(CO2)emissions from motor vehicles have been increasing in large urban centers,thus contributing to air pollution.The general objective of this study is to analyze simulations of CO2 emissions among students who use public transport in the city of Passo Fundo,in the state of Rio Grande do Sul,Brazil.Methodologically,this study was developed in three stages:(1)verification of the time used by higher education students at Faculdade Meridional(IMED)in relation to urban mobility;(2)understanding of the transport mode used by these students,taking into account the one which appears with the highest incidence;(3)simulating CO2 emissions from the modes of transport used by students after the application of the Environmental Impact Simulator(EIS)used by the National Public Transport Association(ANTP,in Portuguese)for the modeling of transport indexes aimed at analyzing CO2 emissions.When considering the total of 3,079 students who assigned to a sample of 66 questionnaires,a reduction of CO2 emissions of 4,527.04 kg during a year in relation to the use of public transport was found.

The role of intelligent technology in the development of urban air mobility systems:A technical perspective

Urban Air Mobility(UAM)is an emerging transportation system that aims at revolutionizing urban mobility through the deployment of small electric vertical takeoff and landing(eVTOL)aircraft.The development of UAM is largely driven by advances in Intelligent Technology(IT).This review article provides an overview of the UAM system and discusses the application of IT in UAM.Major challenges facing UAM are also identified,and an outlook on the future of this promising transportation system is presented.Our main conclusions suggest that IT is a fundamental driver of UAM,enabling a range of applications such as air traffic management and autonomous drone control.However,the UAM system is facing a number of challenges,including eVTOL technology,system integration issues,and noise pollution.Despite these challenges,the future of UAM appears promising;as a disruptive transportation mode,UAM is expected to play an important role in addressing the growing demand of urban transportation in the coming decades.

A multi process value-based reinforcement learning environment framework for adaptive traffic signal control

Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully usedwhen Simulation of Urban MObility(SUMO)is used as an environment simulator for RL.We propose a multi-process framework under value-basedRL.First,we propose a shared memory mechanism to improve exploration efficiency.Second,we use the weight sharing mechanism to solve the problem of asynchronous multi-process agents.We also explained the reason shared memory in ATSC does not lead to early local optima of the agent.Wehave verified in experiments the sampling efficiency of the 10-process method is 8.259 times that of the single process.The sampling efficiency of the 20-process method is 13.409 times that of the single process.Moreover,the agent can also converge to the optimal solution.

Monitoring travel patterns in German city regions with the help of mobile phone network data

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning.Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes,a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access.Concepts need to serve the evaluation of policy objectives,for example in regional or local area plans.In this study,we,therefore,extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns.To accomplish this,we link mobile phone records with urban classifications and transport network data,using both visual and computational approaches to mine the data.The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany,testing hypotheses of transit-oriented regional development,as well as testing for congestion risks in the transport network.The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.