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1. Anatomy of perturbed traffic networks during urban flooding

   https://doi.org/10.1016/j.scs.2023.104693  

   Rajput, Akhil Anil ; Nayak, Sanjay ; Dong, Shangjia ; Mostafavi, Ali ( October 2023 , Sustainable Cities and Society)

   Urban flooding disrupts traffic networks, affecting mobility and disrupting residents’ access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network’s flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods’ impacts on the functioning of traffic networks in terms of travel time and traffic network geometry. 

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2. Detecting Vehicle Illegal Parking Events using Sharing Bikes' Trajectories

   https://doi.org/10.1145/3219819.3219887  

   He, Tianfu ; Bao, Jie ; Li, Ruiyuan ; Ruan, Sijie ; Li, Yanhua ; Tian, Chao ; Zheng, Yu ( January 2018 , the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)

   Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. Traditional approaches to detect illegal parking events rely highly on active human efforts. However, these approaches are extremely ineffective to cover a large city. The massive and high quality sharing bike trajectories from Mo- bike offer us with a unique opportunity to design a ubiquitous illegal parking detection system, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. Two main components are employed in the proposed illegal park- ing detection system: 1) trajectory pre-processing, which filters outlier GPS points, performs map-matching and builds trajectory indexes; and 2) illegal parking detection, which models the normal trajectories, extracts features from the evaluation trajectories and utilizes a distribution test-based method to discover the illegal parking events. The system is deployed on the cloud, and used by Mo- bike internally. Finally, extensive experiments and many insightful case studies are presented. 

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3. Substantial Decreases in U.S. Cities’ Ground-Based NO2 Concentrations during COVID-19 from Reduced Transportation

   https://doi.org/10.3390/su13169030  

   Heintzelman, Asrah ; Filippelli, Gabriel ; Lulla, Vijay ( August 2021 , Sustainability)

   null (Ed.)

   A substantial reduction in global transport and industrial processes stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. This has implications for human health, given the role that this gas plays in pulmonary disease and the findings that past exposure to air pollutants has been linked to the most adverse outcomes from COVID-19 disease, likely via various co-morbidities. To explore how much COVID-19 shutdown policies impacted urban air quality, we examined ground-based NO2 sensor data from 11 U.S. cities from a two-month window (March–April) during shutdown in 2020, controlling for natural seasonal variability by using average changes in NO2 over the previous five years for these cities. Levels of NO2 and VMT reduction in March and April compared to January 2020 ranged between 11–65% and 11–89%, consistent with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link closely, we gathered detailed traffic count data in one city—Indianapolis, Indiana—and found a strong correlation (0.90) between traffic counts/classification and vehicle miles travelled, a moderate correlation (0.54) between NO2 and traffic related data, and an average reduction of 1.11 ppb of NO2 linked to vehicular data. This finding indicates that targeted reduction in pollutants like NO2 can be made by manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future. 

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4. Equity Impacts of the London Congestion Charging Scheme: An Empirical Evaluation Using Synthetic Control Methods

   https://doi.org/10.1177/03611981221138801  

   Craik, Lauren ; Balakrishnan, Hamsa ( May 2023 , Transportation Research Record: Journal of the Transportation Research Board)

   Congestion pricing has been long-believed to effectively regulate traffic in urban city centers. Practical implementations of such policies have been hindered by concerns that they would disproportionately and adversely affect low-income groups. This paper analyzes the impacts of two price increases to the congestion charge on different income groups in Central London, making use of the synthetic control method to leverage empirical data from the UK National Travel Survey. We estimate that the highest income earners contributed to more of the revenue than the lowest income earners, making the scheme progressive in the scale of its equity impact. Although high-income travelers appeared to drop more charge-eligible trips as the price increased, their total trips to Central London did not decrease, suggesting that they were able to substitute with non-chargeable modes of travel. Low-income travelers saw large declines across both chargeable and non-chargeable modes, revealing a much lower rate of substitution. The low-income group responded more to the 2011 price increase than the 2014 one, demonstrating the diminishing ability of subsequent price increases to regulate demand. 

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5. Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system

   https://doi.org/10.1111/mice.13007  

   Yang, Liangze ; Wong, S. C. ; Ho, H. W. ; Shu, Chi‐Wang ; Zhang, Mengping ( April 2023 , Computer-Aided Civil and Infrastructure Engineering)

   In recent decades, the effects of vehicle emissions on urban environments have raised increasing concerns, and it has been recognized that vehicle emissions affect peoples’ choice of housing location. Additionally, housing allocation patterns determine people's travel behavior and thus affect vehicle emissions. This study considers the housing allocation problem by incorporating vehicle emissions in a city with a single central business district (CBD) into a bilevel optimization model. In the lower level subprogram, under a fixed housing allocation, a predictive dynamic continuum user‐optimal (PDUO‐C) model with a combined departure time and route choice is used to study the city's traffic flow. In the upper level subprogram, the health cost is defined and minimized to identify the optimal allocation of additional housing units to update the housing allocation. A simulated annealing algorithm is used to solve the housing allocation problem. The results show that the distribution of additional housing locations is dependent on the distance and direction from the CBD. Sensitivity analyses demonstrate the influences of various factors (e.g., budget and cost of housing supply) on the optimized health cost and travel demand pattern.

    

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