Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.

Looking into shopping behavior and attitudes during the early transition phase of the COVID-19 pandemic

As Covid-19 vaccination in the U.S. begins and hopes of a gradual return to normalcy are raised after much disruption in the shopping behavior of consumers, there is a need to examine consumers' shopping patterns at different stages of the pandemic to adequately understand the potential impacts on shopping behavior. This study explored the shopping behavior of Florida residents during the early transition phase of the pandemic, using data collected from an online survey from February to April 2021. A comprehensive analysis was conducted examining the shopping patterns in terms of purchase frequency, expenditure, and shopping trip distance as well as individuals' shopping attitudes. Further analysis of the shopping behavior was also carried out to investigate whether and how key demographic variables, including age, income, and gender, might be associated with their shopping patterns and attitudes. The analysis reveals that finding and comparing products were big motivators for using online shopping, while concerns about putting personal information online, shipping costs, and the return process may discourage online shopping. Women spent less than men but had significantly higher purchase frequencies than men. People generally liked shopping without interacting with anyone, which suggests that in-store shopping was not necessarily motivated by the need for social interactions. However, price, safety, and parking availability were the top factors in choosing the stores to shop from. Thus, urban and transportation planners should promote compact, mixed-use development and parking management strategies to reduce vehicle shopping trips and the need for separate trips for different purposes. © 2023 ICE Publishing: All rights reserved.

Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

Planning cities for pandemics: A review of urban and transport planning lessons from COVID-19

For the past years, the world has been facing one of the worst pandemics of modern times. The COVID-19 outbreak joined a long list of infectious diseases that turned pandemic, and it will most likely leave scars and change how we live, plan, and manage the urban space and its infrastructures. Many fields of science were called into action to mitigate the impacts of this pandemic, including spatial and transport planning. Given the large number of articles recently published in these research areas, it is time to carry out an overview of the knowledge produced, synthesising, systematising, and critically analysing it. This article aims to review how the urban layout, accessibility and mobility influence the spread of a virus in an urban environment and what solutions exist or have been proposed to create a more effective and less intrusive response to pandemics. This review is split into two avenues of research: spatial planning and transport planning, including the direct and indirect impact on the environment and sustainability. © 2023 ICE Publishing: All rights reserved.

Assessing the Impact of Disruptive Events on Urban Mobility: A Case Study of Chicago Taxis during COVID-19

The rise of Transportation Network Companies (TNCs) over the last decade has significantly disrupted the taxi industry. Studies have shown that taxi ridership has plummeted, and their capacity utilization rates are lower than 50% in five major U.S. cities. Additionally, the COVID-19 pandemic has dealt a severe blow to the already struggling taxi industry. To monitor the evolution of the taxi industry and its impacts on society, our study evaluates changes in the utilization rates, fuel consumption, and emissions among Chicago taxis, using taxi data with rich information on trip profiles from pre-pandemic and pandemic times. Our findings indicate that the taxi utilization rate decreased during the pandemic. While fuel consumption and emissions per kilometer decreased thanks to the reduced traffic during the pandemic, the overall fuel consumption and emissions increased due to increased deadhead travel. The methods developed in this study can be applied to monitor and evaluate the impact of future disruptive events on urban mobility and transportation systems more effectively. By utilizing mobility data to better understand transportation systems, we can develop more efficient, sustainable, and resilient mobility solutions for smart cities. © 2023 ACM.

Concluding Remarks: European Narratives About the Effects of the COVID-19 Pandemic on Coworking

This book is a collection of narrations about the effects of the COVID-19 pandemic from different countries collected within the Workgroup 2ATLAS of the COST action CA18214 ‘The Geography of New Working Spaces and the Impact on the Periphery'. This conclusive chapter comprehends the previous chapters and offers a comparative view regarding the effects on Coworking Spaces (CS), Governmental Measurements to curb the Pandemic, Effects on Work, Remote/Telework Work, Working-From-Home (WFH), Effects on Commuting, Transportation Mods and Services, Effects on the Housing, Place of Residence, Office and Real Estate Market, Effects on Tourism, Effects on Urban Planning. The final section of this chapter draws attention to the direct and indirect effects of coworking spaces. Direct effects on individuals and indirect effects as living-, work- and build-environment, taking into account space and economy, environment (energy) and urban planning. This book contributes to a fast-growing amount of literature on new working spaces, especially coworking spaces. Further empirical studies should be conducted to create evidence as a solid foundation for policies at the EU, national and subnational levels. © 2023, The Author(s).

COVID-19 and the Indoor Environment - observations from a joint National Academies of Engineering seminar series

The SARS-CoV-2 virus and its variants and COVID-19 disease have affected every aspect of society. The US National Academy of Sciences has been providing scientific insights and advice to aid policymakers and researchers in their quest to respond to the pandemic. Since 2020, it has produced numerous reports and workshop proceedings intended to integrate science into national preparedness and response decision-making, to explore lessons learned and best practices from previous preparedness and response efforts, and to consider strategies for addressing misinformation (NASEM, 2021). Among these was a 2021 symposium series that analyzed engineering's role in catalyzing COVID-19 response, recovery, and resilience, examining topics including the mitigation of exposure in public transit systems, engineering solutions to managing pathogens indoors, and the factors influence the transmission of infectious diseases in cities. Speaker presentations addressing these indoor environment topics are summarized here. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

A Sustainable and Resilient Urban Transportation System

Pandemics and other disasters significantly impact community and transportation system through disruptions in normal day-to-day activities, loss or damage to life, property, or environment. The majority of covid-19 cases are from urban areas demonstrating the urgent need for improving cities' resiliency to prepare for pandemics. The urban sustainable development goal (SDG) 11, namely, "make cities and human settlements inclusive, safe, resilient and sustainable” depicts the importance of resilient cities and resilient urban transportation system as well. Urban resilience is defined as urban system's ability to absorb shocks (sudden social, economic, or environmental changes), to adapt to changes, and transform into a new system when coping becomes difficult in the current existing form. The urban transportation system needs to be resilient to deal with pandemics, natural, and biological hazards. The transportation system also supports pandemic propagation such as covid-19 through associated local and long-distance travel risks. Generally, long-distance travel introduces disease into a non-affected community, and the local travel disperses it. A resilient urban transportation system would lead to a resilient city and decrease the negative impacts of pandemic and other calamities such as urban floods, climate risks, etc. Hence it is of utmost importance to evaluate the level of resilience of the urban transportation system and understand how a resilient and sustainable urban transportation system helps in achieving SDG 11. The objectives of the chapter are to;identify how urban transportation connects with SDG 11, develop a framework of indicators to evaluate the resilience of the urban transportation system and its association with SDG 11, and highlight how the resilient urban transportation system would cope with disasters. To develop an indicator framework, indicators are examined from relevant literature. Further, the chapter includes measurements for selected indicators. The above indicator framework would help practitioners and policymakers in the selection of suitable transportation interventions. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer 2022.

Key Levers to Reform Non-Motorized Transport: Lessons From the COVID-19 Pandemic

The objective of this research was to understand key levers that enabled city, regional, and national governments to improve non-motorized transport (NMT) infrastructure during the lockdowns necessitated by the COVID-19 pandemic. The research focused primarily on cycling and adopted a case study approach focusing on three cities: Bengaluru (India), Bogota (Colombia), and London (UK). The selected cities were chosen for diversity across geographies, country income levels, and the scale of interventions. Eight key levers were identified to understand how cycling interventions can be supported, implemented, sustained, and scaled up. These included institutional and organizational arrangements;technical capacity;financing;leadership;policy and regulatory framework;plans, strategies, and technical resources;role of civil society;and communications, messaging, and outreach. The research used secondary literature reviews and key informant interviews, which were validated through an online round table. Research revealed that certain levers were necessary in initiating and continuing successful NMT interventions. These included supportive leadership, participative civil society, and adequate financial and technical capacity. Communications and outreach helped bring behavioral change amongst residents while a coordinated institutional framework and plans and strategies were necessary to sustain momentum. This research contributes to urban mobility and public administration literature in understanding processes and enablers of sustainable mobility interventions. It is relevant for cities in low-and middle-income countries beginning to focus on NMT interventions to combat climate change and public health challenges. © National Academy of Sciences: Transportation Research Board 2021.

Shared E-Scooter Trajectory Analysis During the COVID-19 Pandemic in Austin, Texas.

By March of 2020, most cities worldwide had enacted stay-at-home public health orders to slow the spread of COVID-19. Restrictions on nonessential travel had extensive impacts across the transportation sector in the short term. This study explores the effects of COVID-19 on shared e-scooters by analyzing route trajectory data in the pre- and during-pandemic periods in Austin, TX, from a single provider. Although total shared e-scooter trips decreased during the pandemic, partially owing to vendors pulling out of the market, this study found average trip length increased, and temporal patterns of this mode did not meaningfully change. A count model of average daily trips by road segment found more trips on segments with sidewalks and bus stops during the pandemic than beforehand. More trips were observed on roads with lower vehicle miles traveled and fewer lanes, which might suggest more cautious travel behavior since there were fewer trips in residential neighborhoods. Stay-at-home orders and vendor e-scooter rebalancing operations inherently influence and can limit trip demand, but the unique trajectory data set and analysis provide cities with information on the road design preferences of vulnerable road users.

Equity Implications of Ride-Hail Travel during COVID-19 in California.

COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and-for those who cannot work from home-to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode-ride-hailing-fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Decline in Traffic Congestion Increased Crash Severity in the Wake of COVID-19.

Highway fatalities are a leading cause of death in the U.S. and other industrialized countries. Using highly detailed crash, speed, and flow data, we show highway travel and motor vehicle crashes fell substantially in California during the response to the COVID-19 pandemic. However, we also show the frequency of severe crashes increased owing to lower traffic congestion and higher highway speeds. This "speed effect" is largest in counties with high pre-existing levels of congestion, and we show it partially or completely offsets the "VMT effect" of reduced vehicle miles traveled on total fatalities. During the first eleven weeks of the COVID-19 response, highway driving decreased by approximately 22% and total crashes decreased by 49%. While average speeds increased by a modest 2 to 3 mph across the state, they increased between 10 and 15 mph in several counties. The proportion of severe crashes increased nearly 5 percentage points, or 25%. While fatalities decreased initially following restrictions, increased speeds mitigated the effect of lower vehicle miles traveled on fatalities, yielding little to no reduction in fatalities later in the COVID period.

Analysis of Public Transportation in Montevideo, Uruguay During the COVID-19 Pandemic

This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

The rapid growth of urban areas and population as well as associated development over recent decades have been a major factor controlling ambient air quality of the urban environment in Kerala (India). Being located at the southwestern fringe of the Indian peninsula, Kerala is one of the regions that has been significantly influenced by the activities in the Indian Ocean. The present study focuses on the effect of the COVID-19 lockdown (in 2021) on ambient air quality in the selected coastal metropolitan areas of Kerala. Although previous research studies reported improvement in ambient air quality in Kerala during the lockdown period, this study demonstrates the potential of onshore transport of air pollutants in controlling the air quality of coastal urban regions during the lockdown period. Data from the ambient air quality monitoring stations of the Kerala State Pollution Control Board in the urban areas of Thiruvananthapuram (TM), Kollam (KL), Kozhikode (KZ), and Kannur (KN) are used for the analysis. Temporal variation in the concentration of air pollutants during the pre-lockdown (PRLD), lockdown (LD), and post-lockdown (PTLD) periods (i.e., 1 March to 31 July) of 2021 is examined to assess the effect of lockdown measures on the National Air Quality Index (AQI). Results indicate a significant decline in the levels of air pollutants and subsequent improvement in air quality in the coastal urban areas. All the effect of lockdown measures has been evident in the AQI, an increase in the concentration of different pollutants including CO, SO2, and NH3 during the LD period suggests contributions from multiple sources including onshore transport due to marine traffic and transboundary transport. © 2023, The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences.

Urban Transportation Concept and Sustainable Urban Mobility in Smart Cities: A Review

In order to create a sustainable future for the urban environment in s=Smart cities, it is necessary to develop a concept of urban transport, partially reduce the use of traditional transport, primarily cars, as well as the environmental pressure on society, which is essential to move to a sustainable urban future. In the latest discussions on the future of the urban transport system, the quality of the environment, and the possibility of its improvement are discussed, this issue became especially relevant with the onset of the pandemic, when the lockdowns were introduced. The problem of sustainable transport in urban areas has been recognized in academic studies, searching for appropriate models and solutions. The article presents the latest literature review and illustrates the newest trends with several examples. VOS Viewer software has been used to classify the different keywords, according to their co-citation, following clustering techniques. By analyzing the research conducted by other researchers, it has been possible to structure the ecosystem and trends in the Urban Transportation Concept, also mentioning likely future trends. Based on the literature analysis of the Sustainable Urban Transport, the authors of the study found that a large group of researchers deal with technical solutions and innovative business models, while the essential behavioral aspects are examined in less detail. Extensive literature analysis allowed the authors to select several solutions to achieve the transformation towards sustainable transportation in urban areas: new vehicle technologies and their environmental factors' analysis, geographic information systems, the analytic hierarchy process method, the time series analysis of road traffic accidents using multiplicative models, electrification and use of Friedman Analysis of Variance by Ranks, as well as innovations in sharing mobility.

Sustainable Urban Freight for Energy-Efficient Smart Cities—Systematic Literature Review

Smart cities need energy-efficient and low-emission transportation for people and goods. Most studies focus on sustainable urban-transportation systems for passengers. Freight transportation in cities has increased significantly during the COVID-19 pandemic, leading to greenhouse gases emissions and negative externalities, such as traffic congestion. The purpose of this paper is to identify through a systematic literature review which innovations (hardware and software) applied by logistics service providers (LSPs) in sustainable urban freight (SUF) are suitable to support the transition to energy-efficient smart cities. We propose to classify the existing innovations in last-mile delivery for SUF into categories: (1) urban freight consolidation and/or trans-shipment;(2) the Consumer as a Service Provider (CaaSP);(3) choice of transportation modes. We introduce the concept of CaaSP as an innovative solution in last-mile delivery (LMD), where customers take over some transport operations with the use of smart technologies, and thus reduce the energy demand. We consider the modes of transportation, such as: drones, autonomous delivery robots, autonomous vehicles, cargo bikes (including e-cargo bikes, e-tricycles), electric vehicles (mainly vans), and combined passenger-and-cargo transportation rapid-transit systems. From the analyzed dataset, we find that energy-efficiency in smart cities can be improved by the consolidation of parcels in micro-depots, parcel lockers, and mobile depots. We analyze smart technologies (the Internet of things, big data, artificial intelligence, and digital twins), which enable energy efficiency by reducing the energy demand (fuel) of SUF, due to better operational planning and infrastructure sharing by logistics service providers. We propose a new IEE matrix as an actionable tool for the classification of innovations applied by LSPs in SUF, according to the level of their interconnectivity and energy efficiency. Additionally, this paper contributes to the theory by exploring possible future research directions for SUF in energy-efficient smart cities. © 2023 by the authors.

EVALUATION OF THE IMPACT OF COVID-19 PANDEMIC ON TRANSPORTATION: A CASE STUDY OF IRAN

Coronavirus first appeared in January 2020 and has spread dramatically in most parts of the world. In addition to exerting enormous impacts on public health and well-being, it has also affected a broad spectrum of industries and sectors, including transportation. Countries around the world have imposed restrictions on travel and participation in activities due to the outbreak of the virus. Many countries have adopted social distancing rules requiring people to maintain a safe distance. Therefore, the pandemic has accelerated the transition into a world in which online education, online shopping, and remote working are becoming increasingly prevalent. Every aspect of our life has witnessed a series of new rules, habits, and behaviours during this period, and our travel choices or behaviours are no exception. Some of these changes can be permanent or have long-lasting effects. To control this situation, these changes must first be recognised in various aspects of transportation in order to provide policies for similar situations in the future. In this regard, this study seeks to examine how transportation sectors have changed in the first waves of the pandemic. Iran has been selected as the case study in this paper. This research is divided into two parts. The first part focuses on the effects of the Coronavirus pandemic on rural transportation in Iran. This is followed by assessing the impacts of the virus on urban transportation in Tehran (the capital of Iran). The behaviour of more than 700 travellers in terms of trip purpose, travel time, and mode choice is evaluated using a questionnaire. Results indicate that the number of passengers has reduced dramatically in rural transportation systems. In such systems, considerations such as keeping social distancing, disinfection of passengers and their luggage, and unemployment of a group of personnel working in the transportation industry have been more evident. In urban transportation, education trips have dropped the most. This might relate to an increase in online teaching and health concerns. The same pattern can be seen in the passengers who used bicycles, public taxis, and other public transportation systems. Finally, during the pandemic, drivers' speed has increased, which justifies the need for traffic calming for drivers. © 2022 Warsaw University of Technology. All rights reserved.

Reliability-Oriented Route Generation Algorithm for Multimodal Transport: A Perspective from Supply Chain Reliability Enhancement

Sudden infectious diseases and other malignant events cause excessive costs in the supply chain, particularly in the transportation sector. This issue, along with the uncertainty of the development of global epidemics and the frequency of extreme natural disaster events, continues to provoke discussion and reflection. However, transport systems involve interactions between different modes, which are further complicated by the reliable coupling of multiple modes. Therefore, for the vital subsystem of the supply chain-multimodal transport, in this paper, a heuristic algorithm considering node topology and transport characteristics in a multimodal transport network (MTN): the Reliability Oriented Routing Algorithm (RORA), is proposed based on the super-network and improved k-shell (IKS) algorithm. An empirical case based on the Yangtze River Delta region of China demonstrates that RORA enables a 16% reduction in the boundary value for route failure and a reduction of about 60.58% in the route cost increase compared to the typical cost-optimal algorithm, which means that RORA results in a more reliable routing solution. The analysis of network reliability also shows that the IKS values of the nodes are positively correlated with the reliability of the MTN, and nodes with different modes may have different transport reliabilities (highest for highways and lowest for inland waterways). These findings inform a reliability-based scheme and network design for multimodal transportation. Practical Applications: Recently, the COVID-19 epidemic and the frequency of natural disasters such as floods have prompted scholars to consider transport reliability. Therefore, efficient and reliable cargo transportation solutions are crucial for the sustainable development of multimodal transport in a country or region. In this paper, a new algorithm is designed to obtain a reliability-oriented optimal routing scheme for multimodal transport. Using actual data from the Yangtze River Delta region of China as an example for experimental analysis, we obtain that: (1) the proposed algorithm is superior in terms of efficiency, accuracy, and route reliability, which means that the new algorithm can quickly find more reliable routing solutions in the event of urban transport infrastructure failures;and (2) highway hubs have the greatest transport reliability. Conversely, inland waterway hubs are the least reliable. The influence of national highways and railways on the multimodal transport system is unbalanced. These findings provide decision support to transport policymakers on reliability. For example, transport investments should be focused on building large infrastructure and increasing transport capacity, strengthening the connectivity of inland waterway hubs to hubs with higher transport advantages, and leveraging the role of large hubs. © 2023 American Society of Civil Engineers.

Bus Network Design and Frequency Setting in the Post-COVID-19 Pandemic: The Case of London

A transit network design frequency setting model is proposed to cope with the postpandemic passenger demand. The multiobjective transit network design and frequency setting problem (TNDFSP) seeks to find optimal routes and their associated frequencies to operate public transport services in an urban area. The objective is to redesign the public transport network to minimize passenger costs without incurring massive changes to its former composition. The proposed TNDFSP model includes a route generation algorithm (RGA) that generates newlines in addition to the existing lines to serve the most demanding trips, and passenger assignment (PA) and frequency setting (FS) mixed-integer programming models that distribute the demand through the modified bus network and set the optimal number of buses for each line. Computational experiments were conducted on a test network and the network comprising the Royal Borough of Kensington and Chelsea in London. © 2023 American Society of Civil Engineers.