Quantitative Framework for Establishing Low-Risk Inter-District Travel Corridors During COVID-19.

Aspirations to slow down the spread of novel Coronavirus (COVID-19) resulted in unprecedented restrictions on personal and work-related travels in various nations across the globe in 2020. As a consequence, economic activities within and across the countries were almost halted. As restrictions loosen and cities start to resume public and private transport to revamp the economy, it becomes critical to assess the commuters' travel-related risk in light of the ongoing pandemic. The paper develops a generalizable quantitative framework to evaluate the commute-related risk arising from inter-district and intra-district travel by combining nonparametric data envelopment analysis for vulnerability assessment with transportation network analysis. It demonstrates the application of the proposed model for establishing travel corridors within and across Gujarat and Maharashtra, two Indian states that have reported many COVID-19 cases since early April 2020. The findings suggest that establishing travel corridors between a pair of districts solely based on the health vulnerability indices of the origin and destination discards the en-route travel risks from the prevalent pandemic, underestimating the threat. For example, while the resultant of social and health vulnerabilities of Narmada and Vadodara districts is relatively moderate, the en-route travel risk exacerbates the overall travel risk of travel between them. The study provides a quantitative framework to identify the alternate path with the least risk and hence establish low-risk travel corridors within and across states while accounting for social and health vulnerabilities in addition to transit-time related risks.

Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity.

Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.

Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies.

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio's Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

Do Residents Living in Transit-Oriented Development Station Catchment Areas Travel More Sustainably? The Impacts of Life Events

Transit-oriented development (TOD) is an urban designed model aimed at attracting more sustainable travellers. However, not all TOD projects succeed in maintaining a high rate of sustainable travel behaviour. To examine the impacts of TOD on residents' travel behaviour, this paper applies binary logistic regression to analyse survey data for 1,298 residents living in the TOD areas in Hangzhou collected in 2020. The results show that socioeconomic characteristics, built environment factors, and travel attitudes play important roles in influencing their travel mode choices. Furthermore, the number of children in households and higher levels of car ownership significantly influence residents' sustainable travel behaviours. However, it appears that only a limited number of factors can convince car users to shift to sustainable modes of travel, such as their workplace being accessible by metro and attitudes towards changes in accessibility. This research study contributes to the existing literature in terms of enhancing the understanding of travel mode choice behaviours, particularly with regard to people who live near public transport infrastructure, as well as formulating evidence-based TOD policies to achieve more sustainable transport systems.

Understanding nonlinear and synergistic effects of the built environment on urban vibrancy in metro station areas

Transit-oriented development (TOD) has long been recognized as a significant model for prospering urban vibrancy. However, most studies on TOD and urban vibrancy do not consider temporal differences or the nonlinear effects involved. This study applies the gradient boosting decision tree (GBDT) model to metro station areas in Wuhan to explore the nonlinear and synergistic effects of the built-environment features on urban vibrancy during different times. The results show that (1) the effects of the built-environment features on the vibrancy around metro stations differ over time;(2) the most critical features affecting vibrancy are leisure facilities, floor area ratio, commercial facilities, and enterprises;(3) there are approximately linear or complex nonlinear relationships between the built-environment features and the vibrancy;and (4) the synergistic effects suggest that multimodal is more effective at leisure-dominated stations, high-density development is more effective at commercial-dominated stations, and mixed development is more effective at employment-oriented stations. The findings suggest improved planning recommendations for the organization of rail transport to improve the vibrancy of metro station areas.

Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic

With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.

A method for assessing the COVID-19 infection risk of riding public transit

During the pandemic, to prevent the spread of the virus, countries all adopted various safety measures, including masking, social distancing, and vaccination. However, there is a lack of methods that can quantitively evaluate the effectiveness of these countermeasures. This research first develops a model to quantitively evaluate the infection risk of riding public transit. By utilizing the developed model, the effectiveness of different countermeasures could be evaluated and compared. For demonstration purposes, the developed model is applied to a particular bus route in the City of Houston, Texas. The modeling results show that masking, social distancing, and vaccination can all reduce the infection risk for passengers. And among all these countermeasures, face masking is the most effective one. In addition, model results approve that the COVID-19 infection risk is highly related to the exposure time and the risk can be controlled by reducing the exposure time. Thus, a new strategy named the "split route strategy” is proposed and compared with the "capacity reduction strategy” using the model developed. In addition, a cost-benefit analysis is performed to assess the feasibility of the proposed "split route strategy”. Furthermore, two interviews were conducted with practitioners at Houston Metro. Both interviewees believe that face masking could significantly prevent the spread of the virus, which validated the model results. © 2022

T-Ridership: A web tool for reprogramming public transportation fleets to minimize COVID-19 transmission.

As the outbreak of novel coronavirus disease (COVID-19) continues to spread throughout the world, steps are being taken to limit the impact on public health. In the realm of infectious diseases like COVID-19, social distancing is one of the effective measures to avoid exposure to the virus and reduce its spread. Traveling on public transport can meaningfully facilitate the propagation of the transmission of infectious diseases. Accordingly, responsive actions taken by public transit agencies against risk factors can effectively limit the risk and make transit systems safe. Among the multitude of risk factors that can affect infection spread on public transport, the likelihood of exposure is a major factor that depends on the number of people riding the public transport and can be reduced by socially distanced settings. Considering that many individuals may not act in the socially optimal manner, the necessity of public transit agencies to implement measures and restrictions is vital. In this study, we present a novel web-based application, T-Ridership, based on a hybrid optimized dynamic programming inspired by neural networks algorithm to optimize public transit for safety with respect to COVID-19. Two main steps are taken in the analysis through Metropolitan Transportation Authority (MTA): detecting high-density stations by input data normalization, and then, using these results, the T-Ridership tool automatically determines optimal station order to avoid overcrowded transit vehicles. Effectively our proposed web tool helps public transit to be safe to ride under risk of infections by reducing the density of riders on public transit vehicles as well as trip duration. These results can be used in expanding on and improving policy in public transit, to better plan the scheduled time of trains and buses in a way that prevents high-volume human contact, increases social distance, and reduces the possibility of disease transmission (available at:http://t-ridership.com and GitHub at: https://github.com/Imani-Saba/TRidership).

Understanding the Relation of Psychological/Behavioral Factors and Cycling During the Covid-19 Pandemic: A Case Study in Iran

Previous research on travel behavior has concentrated on the behavior of traveling by cars, especially by private vehicles, while the research on cycling has focused on cycling infrastructure, the built environment, and the natural environment. Furthermore, the studies conducted during pandemics are mostly based on behavioral changes in motorized transportation. The present research tries to identify and evaluate the variables influencing cyclist behavior during covid-19 pandemic. In this research, the sample size retrieved from a survey of 375 participants was checked with Cronbach's alpha standard and estimated using confirmatory factor analysis. Results show that the variables related to health protocols can greatly impact knowing the behavior of cyclists in the time of Covid-19. Furthermore, the results show that the health issues of shared bikes can be an obstacle for people to use them more. © 2023, The Author(s), under exclusive licence to Intelligent Transportation Systems Japan.

Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method. IEEE

First Year of COVID-19. The Impact of Pandemic Waves on Public Transport Usage in Cluj-Napoca, Romania

The global pandemic of COVID-19 has had a significant influence on public transportation usage and service provision. As many countries begin to return to normality, new public transportation planning standards are being developed. Considering these new standards, there is a critical shortage of understanding the possible impact of the pandemic on public transportation systems and models that can help service planning face these challenges. This paper analyzes data collected in Cluj-Napoca (Romania), from late-February 2020 to late-March 2021. As local authorities began to remove restrictions aimed at limiting the propagation of the SARS-CoV-2 virus, the study investigates the travel changes in various modes of transportation, travel plans, and user categories. Results confirm that low-income groups depend on public transit the most;consequently, they had considerably lower declines in usage during the COVID-19 pandemic. This study also identifies various daily average patterns of demand for public transportation in Cluj-Napoca throughout each stage of the pandemic. All of these data contribute to extending the global understanding about COVID-19's influence on transport usage by comparing these outcomes with the ones from other cities. They offer pertinent information for transportation authorities to develop adaptation policies to a major event like this pandemic. Although there is still apprehension about using public transportation, the collected data show that the regular public transport users from before the pandemic have been gradually returning to their transport of choice once the restrictions have been relaxed (March-May 2020).

director's message

Paniati offers his message as ITE Executive Director and CEO. He shares that when they ask their members why they belong to ITE, connecting with their peers is a big part of the answer. Through these personal interactions and with ITEs products and services, members find new practices and solutions allowing them to positively impact their communities. There are many different avenues to make these connections within ITE. During 2023, ITE will be providing a wide variety of opportunities to Connect People and Communities. In Nov, they kicked-off their year-long transportation planning professional membership drive. In just the first month, more than 250 planners have taken advantage of the free membership offer. In Feb, their first All-member survey since 2016 will be conducted, helping ITE benchmark its progress in meeting member needs, serving members in the post COVID-19 environment, and identifing opportunities to improve.

Promoting economic and environmental resilience in the post-COVID-19 era through the city and regional on-road fuel sustainability development

How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles' transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments' effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Assessment of fuel price, exchange rate and unemployment rate impact on road passenger journeys in South Africa

In recent decades passenger transportation journeys experienced a decline and this decline may due to various causes such as cost of transportation, low economic growth, exchange rate volatility, unemployment and petrol price. [...]to the best knowledge of the author, no study was conducted to determine the relationship between the aforementioned variables. [...]the main objective of the current study is to analyse the impact of the exchange rate, petrol price and unemployment rate on road passenger journeys in the South African transportation sector. [...]to minimize either monetary budget or physical and mental burden, irrespective of having their cars, people prefer to use public transport (Guo & Wilson, 2011;Onderwater & Kishoon, 2017). South African road transport depend on the imported fuel and the price of the latter within the domestic market is determined by the exchange rate. [...]the exchange rate is another economic variable that impacts road transpo0rt demand (Havenga et al., 2014).

Evaluation of Using Genetic Algorithm and ArcGIS for Determining the Optimal-Time Path in the Optimization of Vehicle Routing Applications

Transportation is regarded as one of the most important issues currently being researched;this issue needs the search for approaches or processes that might lessen many contemporary traffic concerns. Congestion, pollution, and accidents have escalated lately, negatively impacting urban environments, economic development, and citizens’ lifestyles. The rise of illnesses and epidemics throughout the world, such as COVID-19, has created an urgent need to find the best way to save people’s lives. The vehicle routing problem (VRP) is a well-known moniker for improving transportation systems and is regarded as one of the ancient and contemporary difficulties in route planning applications. One of the main tasks of VRP is serving many customers by determining the optimal route from an initial point to a destination on a real-time road map. The best route is not necessarily the shortest-distance route, but, in emergency cases, it is the route that takes the least fitness cost (time) and the fastest way to arrive. This paper aims to provide an adaptive genetic algorithm (GA) to determine the optimal time route, taking into account the factors that influence the vehicle arrival time and cause delays. In addition, the Network Analyst tool in ArcGIS is used to determine the optimal route using real-time map based on the user’s preferences and suggest the best one. Experimental results indicate that the performance of GA is mainly determined by an efficient representation, evaluation of fitness function, and other factors such as population size and selection method.

Connection between the Spatial Characteristics of the Road and Railway Networks and the Air Pollution (PM10) in Urban–Rural Fringe Zones

Atmospheric particulate matter (PM10) is one of the most important pollutants for human health, and road transport could be a major anthropogenic source of it. Several research studies have shown the impact of roads on the air quality in urban areas, but the relationship between road and rail networks and ambient PM10 concentrations has not been well studied, especially in suburban and rural landscapes. In this study, we examined the link between the spatial characteristics of each road type (motorway, primary road, secondary road, and railway) and the annual average PM10 concentration. We used the European 2931 air quality (AQ) station dataset, which is classified into urban, suburban, and rural landscapes. Our results show that in urban and rural landscapes, the spatial characteristics (the density of the road network and its distance from the AQ monitoring points) have a significant statistical relationship with PM10 concentrations. According to our findings from AQ monitoring sites within the urban landscape, there is a significant negative relationship between the annual average PM10 concentration and the density of the railway network. This result can be explained by the driving wind generated by railway trains (mainly electric trains). Among the road network types, all road types in the urban landscape, only motorways in the suburban landscape, and only residential roads in the rural landscape have a significant positive statistical relationship with the PM10 values at the AQ monitoring points. Our results show that in the suburban zones, which represent the rural–urban fringe, motorways have a strong influence on PM-related air pollution. In the suburban areas, the speed of vehicles changes frequently near motorways and intersections, so higher traffic-related PM10 emission levels can be expected in these areas. The findings of this study can be used to decrease transportation-related environmental conflicts related to the air quality in urban, urban–rural fringe, and rural (agricultural) landscapes.

Carbon Emission Measurement of Urban Green Passenger Transport: A Case Study of Qingdao

Urban passenger transport is one of the most significant sources of fossil energy consumption and greenhouse gas emission, especially in developing countries. The rapid growth of urban transport makes it a critical target for carbon reduction. This paper establishes a method for calculating carbon emission from urban passenger transport including ground buses, private cars, cruising taxis, online-hailing taxis, and rail transit. The scope of the study is determined according to the transportation mode and energy type, and the carbon emission factor of each energy source is also determined according to the local energy structure, etc. Taking into consideration the development trend of new energy vehicles, a combination of “top-down” and “bottom-up” approaches is used to estimate the carbon dioxide emission of each transportation mode. The results reveal that carbon emission from Qingdao’s passenger transport in 2020 was 8.15 million tons, of which 84.31% came from private cars, while the share of private cars of total travel was only 45.66%. Ground buses are the most efficient mode of transport. Fossil fuels emit more greenhouse gases than other clean energy sources. The emission intensity of hydrogen fuel cell buses is better than that of other fuel type vehicles. Battery electric buses have the largest sensitivity coefficient, therefore the carbon emission reduction potentially achieved by developing battery electric buses is most significant.

State of ITE 2022

At the Opening Session of each ITE Annual Meeting and Exhibition, Paniati provides a "State of ITE" report. He wanted to take this opportunity to share the highlights of his presentation in New Orleans: Membership Is Growing -- Despite COVID-19, their membership continues to grow. They expect to exceed 16,500 members in 2022. If recent trends continue, they can reach 17,000 in 2023--which would be the highest membership in the more than 90-year history of ITE. Fueling their growth are new public agency members;the past year they added Caltrans;New York State DOT;Kentucky Transportation Cabinet;Connecticut DOT;City of Atlanta GA;City of Orange CA;City of Victoria, BC and City of Richmond Hill, ON, both in Canada;St. Charles County MO;and Douglas County, Castle Rock CO. With strong fiscal leadership by the International Board of Direction (IBOD) and the ITE staff, they have built their financial reserves from 20 percent of their annual operating budget in 2016 to 100 percent at the end of 2021. The considerable effort to create a more consistent member experience and organizational structure across their Districts, Sections, and Chapters is resulting in more effective Districts and stronger Sections.

Revealed and future travel impacts of COVID-19 in sub-Saharan Africa: Results of big data analysis and a Delphi panel survey

Background: After coronavirus disease 2019 (COVID-19) was declared a pandemic, movement restrictions were implemented across sub-Saharan Africa. There has been much speculation on what the long-term impacts on urban transport might be. Objectives: The aim of this paper is to identify the revealed and future travel impacts of the pandemic. Method: To pursue this aim, evidence was compiled from two sources: secondary big data;and a ( n = 15) two-wave Delphi panel survey of experts in the region. Results: It is predicted that longer-term impacts will take the form of: reduced travel by, and accessibility for, low-income households residing in peripheral locations because of decreased welfare;reduced transport service availability;operator reduction (particularly amongst unsubsidised formal operators);increased remote activity participation for a minority of better resourced households with white-collar workers;and disrupted trip distributions as the mix of city-centre land use changes in response to business attrition in economic recession rather than to disrupted bid rents. Conclusion: The major impact of the pandemic is likely to be on welfare, rather than on trip substitution. There is a need, therefore, to focus policy on the mitigation of these impacts and, more particularly, on ways of measuring changes in transport disadvantage and exclusion so that reliable data are available to inform mitigation strategies. The mitigation strategies considered should include investment in affordable ‘digital connectivity’ as a means of complementing accessibility from physical proximity and mobility. The pandemic also highlights the need to develop more robust transport planning practices to deal with uncertainty.

Motivations and barriers to using high-speed rail: An application of conjoint analysis – Insights from Vietnam

Background: Nowadays, a gradual change in customers’ attitudes towards transport service makes it more challenging to understand the reasons behind customers’ travel decisions. High-speed rail (HSR) has been mentioned recently and is expected as the best and most modern transport option in long-distance trips in Vietnam. However, research studies have paid scant regard to how HSR’s attributes may affect potential users, and therefore the motivations and barriers to adopting HSR are still unknown. Objectives: This study aimed at examining motivations and barriers to take-up HSR for considering customers’ preference on the proper attributes and levels of HSR. Method: This study drew on a nationwide survey and conjoint analysis to investigate customers’ behaviour. Results: In Vietnam’s context, HSR ticket was found to be the principal barrier to adoption, whereas the speed of HSR was identified as the least important behavioural driver amongst potential HSR users. The results show that HSR design and planning should provide a combination of minimum check-in and waiting time, a 20-min frequency, average speed of 250 km/h, all add-on services and facilities, ticketing of approximately VND 500–700 thousand per 300–500 km and nearby all-day parking. Conclusion: This investigation has demonstrated the value of conjoint analysis to compare a wide range of attributes associated with consumers’ decision to use HSR. The findings indicate that in countries such as Vietnam, in particular, where train usage is low, policymakers and transportation agencies seeking to boost the use of HSR must take attributes other than fare into consideration.