Evening Rush Research Articles

How Naturalistic Driving Compares to Aβ‐related CSF and Plasma Biomarkers in the Detection of Amyloid PET Positivity: A Machine Learning Approach

AbstractBackgroundIdentifying individuals with Alzheimer disease (AD) pathology a decade or more before the onset of dementia symptoms can be achieved with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF) biomarkers, or blood biomarkers. This study compares driving behavior with CSF and blood biomarkers to identify amyloid deposition in AD, as defined by the “gold standard” PET‐amyloid.MethodWe used commercial‐in‐vehicle‐GPS dataloggers to study naturalistic driving behaviors among 101 cognitively normal older drivers (aged 65+). All participants had blood, CSF, and PET biomarker data. The cohort included 37 individuals with and 64 without PET‐amyloid positivity.Driving variables included total number of trips, number of trips with a distance less than 1‐mile, trips in evening rush hour, trips with hard acceleration, speeding events, and average speed. Three Multi‐layer Perceptron (MLP) classifiers were trained with three sets of inputs: (1) driving variables, (2) driving variables and age, and (3) driving variables, age, and APOE ε4 status. Additionally, plasma Aβ42/Aβ40 < 0.1013, CSF Aβ42/Aβ40 < 0.0673, or Amyloid Probability Score (APS) (modeled score incorporating plasma Aβ42/40, age, and APOE ε4 status) > 15 were used to detect PET‐amyloid positivity. The driving models were trained on 80% of the data and all biomarkers were evaluated on the remaining 20% of the data. Specificity (Spe), sensitivity (Sen), F1‐score (F1), and area under the receiver operating curve (AUC) were compared across the three driving models and three fluid‐based biomarkers.ResultFor predicting PET‐amyloid positivity, CSF Aβ42/Aβ40 achieved the highest performance (Spe=0.96, Sen=0.98, F1=0.97) followed by the MLP model with driving variables, age, and APOE ε4 status (Spe=0.93, Sen=0.91, and F1=0.92), MLP model with driving variables and age (Spe=0.88, Sen=0.85, and F1=0.86), APS (Spe=0.82, Sen=0.84, and F1=0.81), plasma Aβ42/Aβ40 (Spe=0.79, Sen=0.80, and F1=0.76), and MLP model with driving variables (Spe=0.71, Sen=0.72, and F1=0.71). The AUC from driving variables was 0.800, and improved by adding age to 0.933, and age and APOE ε4 status to 0.977. Furthermore, AUC scores were 0.986, 0.911, and 0.873 for CSF Aβ42/Aβ40, APS, and plasma Aβ42/Aβ40, respectively.ConclusionDriving is a useful neurobehavioral marker in predicting PET‐amyloid positivity among cognitively normal older drivers.

Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control.

Particulate matter exposure at urban traffic intersection during haze episodes: A case study in Changsha.

Urban intersection has been identified as a major contributor to the total personal exposure and short-term high exposure of particulate matter (PM) in modern cities. The main aim of this study was to get a better understanding of the determinants of traffic-related PM temporal variations and personal exposure to PMs at a viaduct-covered intersection controlled by traffic signals during the winter haze episodes. A two-day field sampling campaign was conducted with a portable device during evening rush hour and measured the PMs in the 0.3-10 μm size range both on the surface crosswalk and underground passage. PM variations and related cumulative respiratory deposition dose (RDD) along two routes with six road crossing scenarios were estimated on a severe pollution day and a typical day for both adults and children, respectively. The PM concentration on the severe pollution day ranged 59.2-67.9 μg/m3 for PM1, 163.8-257.0 μg/m3 for PM2, and 258.2-469.1 μg/m3 for PM10, respectively, as compared to 47.9-57.9 μg/m3for PM1, 112.7-199.8 μg/m3 for PM2, and 151.0-301.0 μg/m3 for PM10 on the typical day, respectively. The variability could be explained largely by the built-up environment, traffic component, signal setting, and ventilation condition. Our data suggest that an appropriate setting of the traffic signal would help reduce the personal exposure dose on the surface crosswalk at urban intersections and the ventilation condition had a significant influence on local PM distributions inside the underground passage. Results here provide possible suggestions for the future design of a walkable city.

Influence of traffic on the temperature field of tunnel in cold region: A case study on the world's longest highway spiral tunnel

The impact of the piston wind caused by vehicle traffic on the temperature field distribution of the tunnel cannot be ignored, especially in spiral tunnels, where the temperature field distribution is more complicated than that in straight tunnels. Based on the world's longest highway spiral tunnel, this study explores the influence of traffic on the temperature fields of spiral and straight tunnels, and proposes a new design method for laying length of insulation layer. The results show that traffic has a significant effect on the temperature field distribution in the tunnel. The influence degree of different traffic elements decreases in the following order: average vehicle speed > daily traffic > morning rush hour > evening rush hour. The temperature field distribution of spiral tunnels is asymmetry on the left and right compared with straight tunnels, while the influence degree of traffic on the outside of spiral tunnels is similar to that of straight tunnels. Due to the difference of wind direction and traffic direction at different tunnel openings, an asymmetric insulation layer laying method is proposed. It is suggested that the lengths of the insulation layers of the Jinjiazhuang Tunnel should be 1200 m for the right tunnel inlet, 700 m for the right tunnel outlet, 700 m for the left tunnel inlet, and 1400 m for the left tunnel outlet.

Traffic signal plans to decongest street grids

Two new synchronization strategies are developed for signalized grids of two-directional streets. Both strategies are found to reduce congestion significantly more than do other approaches. One of the strategies is static and the other adaptive. Both use a common timing pattern for all signals on the grid but use a different offset for each. The static strategy serves the morning rush by providing perfect forward progression on all streets in the directions that point toward a reference intersection, one that is located near the center of gravity of all workplaces. For the evening rush, perfect progression is achieved for all travel directions that point away from the reference intersection. The adaptive strategy toggles between this forward synchronization mode and a second mode suited for congestion, but only in a pre-determined district surrounding the reference intersection. Toggling is based on the district's real-time traffic density.The paper shows how to switch quickly between the two synchronization modes without resorting to unacceptably short phases. It also shows that if the grid is formed by two intersecting sets of parallel streets, even if unevenly spaced, then every street can be perfectly synchronized in one of its directions. As a result, an inbound driver in the morning, or an outbound driver in the evening, is guaranteed to encounter synchronized signals over the full length of her trip. Although this is not possible for more irregular grids, the paper shows how to modify the two strategies for this case, so that they still perform well.The strategies were benchmarked with simulations against a fixed, zero-offset strategy for many scenarios, because zero-offsets are known to work well under congestion. In one important scenario representing a severely congested morning rush, both strategies were also benchmarked against a state-of-the-practice computer program. While the state-of-the-practice program reduced the zero-offset delay by a modest 7%, the proposed strategies reduced it by 21% (static) and 32% (adaptive); i.e., improving on the state-of-the-practice program by 14% and 25%. These improvements considerably exceed the 1% to 5% reductions typically reported in the literature for other state-of-the-art methods that have been compared with state-of-the-practice programs. Similarly good results were obtained for the other scenarios, which included the morning and evening rushes, various distributions of workplaces, and both regular and irregular grids.

Characteristics and health risks of parent, alkylated, and oxygenated PAHs and their contributions to reactive oxygen species from PM2.5 vehicular emissions in the longest tunnel in downtown Xi'an, China

A vehicular emission study was conducted in the longest inner-city tunnel in Xi'an, northwestern China in four time periods (I: 07:30–10:30, II: 11:00–14:00, III: 16:30–19:30, and IV: 20:00–23:00 LST). A sum of 40 PAHs, including parent (p-PAHs), alkylated (a-PAHs), and oxygenated (o-PAHs) in fine particulate matter (PM2.5) were quantified. The relationships between the PAHs and the formation of reactive oxygen species (ROS) were also studied. The average total quantified PAHs concentration was 236.3 ± 48.3 ng m−3. The p-PAHs were found to be the most dominated group, accounting for an average of 88.1% of the total quantified PAHs, followed by a-PAHs (6.1%) and o-PAHs (5.8%). On the base of the number of aromatic rings, the groups of ≤5 rings (92.5 ± 1.2%) had higher fractions than the high ones (≥6 rings, 7.5 ± 1.2%) for pPAHs. Diurnal variations of PAHs subgroups exhibited the highest levels in Period III, consistent with the largest traffic counts in evening rush hours. However, less reduction of few PAHs in the night period demonstrates that the emissions of compressed natural gas (CNG) and methanol-fueled vehicles cannot be ignored while their contribution increased. High ROS activity levels were observed in the traffic-dominated samples, implying the potential oxidative damages to humans. Additionally, diurnal variation of the ROS activity was consistent with the total quantified PAHs and toxic equivalency of benzo[a]pyrene. Good correlations (R > 0.6, p < 0.05) were seen between individual groups of PAHs (especially for 3–5 rings p-PAHs, 4 rings a-PAHs, and 2–3 rings o-PAHs) and ROS activity, supporting that the vehicular emitted PAHs possibly initiate oxidative stress. The multiple linear regression analysis further illustrated that chrysene contributed the highest (25.0%) to ROS activity. In addition to highlighting the potential hazards to the PAHs from the vehicular emission, their roles to mitigate the health effects by formations of ROS were firstly reported in northwestern China.

Comparison of Fluorescent Techniques Using Two Enzymes Catalysed for Measurement of Atmospheric Peroxides

Atmospheric peroxides, especially hydrogen peroxide (H2O2), are essential oxidants. The peroxide concentration is closely related to the extent of OH radicals and the O3 cycle in the tropospheric atmospheric chemistry. However, only a few studies have investigated their atmospheric concentrations in China because of inadequacies in the measurement techniques or higher costs of analytical instruments. Therefore, it is essential to design a suitable analysis method of peroxides with higher sensitivity, lower detection limit, and low cost. In view of that, this study investigated the optimum analysis conditions of two H2O2 analytical techniques: the high-performance liquid chromatography (HPLC) with fluorescence detection using two-enzyme catalysis of horseradishperoxidase (HRP method) and Hemin (Hemin method). Furthermore, these two analysis methods were systematically compared in terms of detection limit, calibration curve, precision, accuracy, and applicability for the first time. The findings showed that the HRP method had a lower detection limit, higher sensitivity, and better applicability for detecting H2O2 and methyl hydroperoxide (MHP) than the Hemin method. Moreover, the HRP method is better suitable for H2O2 and MHP detection, which requires low detection limits and high sensitivity. Besides this, the Hemin method is inexpensive and is more suitable for detecting hydroxyl alkyl peroxides (C ≥ 3). The atmospheric concentrations (average) of H2O2 and MHP were 0.60 ± 0.37 ppb and 0.081 ± 0.039 ppb, respectively, as determined by the HRP method. Importantly, atmospheric peroxide concentrations were higher on sunny days than on cloudy days in Beijing in September 2016. H2O2 concentrations showed a diurnal variation with the lowest value in the morning and two peaks at 13:00–17:00. In contrast, MHP concentrations were lowest in the morning and highest after 17:00. Photochemical reactions were responsible for the production of H2O2 and MHP. The reactions of O3 and olefins emitted by motor vehicles also caused H2O2 concentration to increase during the evening rush hour.

Impact of COVID-19 Pandemic Lockdown in Ambient Concentrations of Aromatic Volatile Organic Compounds in a Metropolitan City of Western India.

The real‐time Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX) concentrations were measured in a metropolitan city of India during January to May of 2020 and 2014‐2015‐2018 to assess the impact of emission reduction during the COVID‐19 lockdown. The total BTEX (∑BTEX) concentrations were 11.5 ± 9.0, 15.7 ± 16, 5.3 ± 5.0, 2.9 ± 2.0, and 0.93 ± 1.2 ppbv in January–May 2020, respectively. The evening rush hour peaks of BTEX during lockdown decreased by 4–5 times from the same period of years 2014‐2015‐2018. A significant decline in background concentrations suggests a regional‐scale reduction in anthropogenic emissions. The contributions of ∑TEX compounds to ∑BTEX increased from 42% to 59% in winter to 64%–75% during the lockdown under hot summer conditions. While emission reductions dominated during the lockdown period, the meteorological and photochemical factors may also have contributed. Meteorological influence on actual observed BTEX data was removed by normalizing with ventilation coefficient (VC). The actual ambient air reductions of 85%–90% and VC‐normalized reductions of 54%–88% of the BTEX concentrations during lockdown were estimated compared to those during the same period of 2014‐2015‐2018. The estimated changes using nighttime data, which take into account BTEX photooxidation removal, are ∼8% lower than the VC‐normalized estimates using all data. These significant reductions in BTEX concentrations are consistent with the change in people's movement as inferred from mobility data during the lockdown. Although enforced, the significant decline in ambient BTEX levels during lockdown was a good change for the air quality. The study suggests a need for more effective science‐based policies that consider local and regional factors.

Spatial-temporal prediction of the environmental conditions inside an urban road tunnel during an incident scenario

Nowadays, environmental conditions inside tunnel have attracting more and more public attentions. The occurrence of traffic accident will greatly change the spatiotemporal distribution of traffic flow, and further influence the environmental condition inside tunnel. However, very few research can be found in literature. To fill this gap, a multi-segments model is developed by considering the spatial-temporal characteristics of traffic flow. Detailed field measurements have been conducted in an urban tunnel with car accident during the evening rush hours in a winter day. The air temperature, air velocity, CO concentration, CO2 concentration and PM concentration are measured simultaneously. The proposed theoretical multi-segments model agrees well with the measurement results. The occurrence of the accident directly influences the air velocity and CO concentration near the accident location, and has no noticeable effect on air temperature. The air velocity in the region prior to accident location decreases significantly from 3.7 to 4 m/s to 1.3–1.4 m/s. The massive traffic jam quickly propagates upstream. The CO concentration gradient (ΔC/Δx) after the accident is about 3.4–9 times of the pre-accident value. This research can provide real-time support to the tunnel management and improve air quality inside tunnel.

Kuala Lumpur train collision during the COVID-19 pandemic

BackgroundTwo city trains collided in an underground tunnel on 24 May 2021 at the height of COVID-19 pandemic near the Petronas Towers, Kuala Lumpur, Malaysia, immediately after the evening rush hours. We aim to evaluate the management of this mass casualty incident highlighting the lessons learned to be used in preparedness for similar incidents that may occur in other major cities worldwide.MethodsInformation regarding incident site and hospital management response were analysed. Data on demography, triaging, injuries and hospital management of patients were collected according to a designed protocol. Challenges, difficulties and their solutions were reported.ResultsThe train's emergency response team (ERT) has shut down train movements towards the incident site. Red zone (in the tunnel), yellow zone (the station platform) and green zone (outside the station entrance) were established. The fire and rescue team arrived and assisted the ERT in the red zone. Incident command system was established at the site. Medical base station was established at the yellow zone. Two hundred and fourteen passengers were in the trains. Sixty-four of them were injured. They had a median (range) ISS of 2 (1–43), and all were sent to Hospital Kuala Lumpur (HKL). Six (9.4%) patients were clinically triaged as red (critical), 19 (29.7%) as yellow (semi-critical) and 39 (60.9%) as green (non-critical). HKL's disaster plan was activated. All patients underwent temperature and epidemiology link assessment. Seven (10.9%) patients were admitted to the hospital (3 to the ICU, 3 to the ward and 1 to a private hospital as requested by the patient), while the rest 56 (87.5%) were discharged home. Six (9.4%) needed surgery. The COVID-19 tests were conducted on seven patients (10.9%) and were negative. There were no deaths.ConclusionsThe mass casualty incident was handled properly because of a clear standard operating procedure, smooth coordination between multi-agencies and the hospitals, presence of a 'binary' system for 'COVID-risk' and 'non-COVID-risk' areas, and the modifications of the existing disaster plan. Preparedness for MCIs is essential during pandemics.

Field measurement of thermal environment and air pollution level inside an urban road tunnel during the evening rush hour

Some urban road tunnels serve as the main traffic access routes for residents in the surrounding large communities. The traffic congestion is a common phenomenon during rush hours inside this kind of tunnel. In order to learn about the environmental parameters inside tunnel during the evening rush hour, a comprehensive measurement in a typical commuting tunnel is conducted. The thermal environment parameters and air pollutant distribution along the tunnel are measured simultaneously. The results show that the traffic volume, vehicle speed and air velocity inside tunnel take on the obvious temporal and spatial discrepancy. There are the average 4.58°C air temperature rise and 96μg/m3 PM2.5 concentration rise from the tunnel entrance to the tunnel exit. The PM2.5 concentration at the tunnel exit is about 2.67 times of the 24-hour average limit of Chinese standard. The rise gradients of the air temperature and PM 2.5 concentration during the evening rush hour is given out.

Neurological outcomes of out-of-hospital cardiac arrest occurring in Tokyo train and subway stations

ObjectivesThe purpose of this study was to identify a relationship between the background environment, bystander and emergency medical services intervention, and favourable neurological outcomes (CPC1-2) one-month after out-of-hospital cardiac arrest (OHCA) occurred at Tokyo train and subway stations. MethodsThis retrospective observational study used OHCA data between 2014 and 2018 that occurred at train stations in Tokyo. The eligible 954 patients were analysed for correlation between background, time frame, and location. Multivariable logistic regression models were used to estimate factors associated with CPC1-2 in patients with cardiogenic OHCA. ResultsA total of 886 OHCA cases, cardiogenic (n=562) and non-cardiogenic (n=324), met the inclusion criteria. Of the cardiogenic cases, 71.9% occurred at the platform and on-a-train. One-month CPC1-2 was achieved in 32.0% of cardiogenic OHCAs, which included 47.3% during morning rush hour, 24.7% during daytime hours, 40.2% during evening rush hour, and 20.5% during night-time/early morning hours. CPC1-2 had significant correlation with morning rush hour (adjusted odds ratio [AOR],4.52; 95% confidence interval [CI], 1.09–18.78), evening rush hour (AOR, 6.85; 95% CI, 1.51–31.15), public access defibrillation (AOR, 5.19; 95% CI, 1.38–19.51), and ventricular fibrillation or pulseless ventricular tachycardia (AOR, 7.56; 95% CI, 1.35–42.43). ConclusionA total of 71.9% of cardiogenic OHCAs occurred at platforms and on trains. To improve neurological outcomes of OHCAs at stations, AED installations on train platforms are necessary. Additionally, using artificial intelligence-based platform monitoring for early detection of OHCAs and offering CPR training are required.

Characterization of Black Carbon and Its Correlations with VOCs in the Northern Region of Hangzhou Bay in Shanghai, China

Ozone and PM2.5 (all particulate matter with diameter of 2.5 µm or smaller) are currently two disturbing environmental issues in most cities of China. Black carbon (BC), mainly from incomplete combustion, is one of the most important components of PM2.5 because it can absorb light and contribute to haze pollution and global warming. Meanwhile, volatile organic compounds (VOCs) have become a major air pollutant due to their association with haze, ozone (O3), global warming and human health by direct or indirect processes. In this study, one year-long observation campaign of BC, VOCs and other conventional air pollutants was conducted in the Northern Region of the Hangzhou Bay (NRHB) in Shanghai, China. The results indicated that higher concentration of BC mainly occurred in the autumn and winter, especially in December. In December, higher BC concentrations were found when the air mass came from northwest where there is an important local freeway, or southwest where some adjacent southwest chemical industrial parks are located. Different from the characteristics of BC in urban areas reported by previous studies, the diurnal variation of BC exhibited three peaks, two of which coincided with the morning and evening rush hours which are related to the heavy diesel traffic from a nearby freeway, and the third peak was often found late at night, around 2 am, which might be associated with abnormal emissions from an industrial park or marine traffic in the ocean waterway. BC had weakly negative correlation with O3 and NO, and a strongly positive correlation with PM2.5, SO2, NO2 and NOx, which implies that some incomplete combustion sources might occur in the nearby regions. With regard to VOCs, BC had a strong positive correlation with alkane, alkenes, alkynes, aromatic and non-sulfur VOCs, particularly with aromatic organic matter. Unlike the stronger correlation with aromatics in the morning rush hours, a stronger correlation between BC and alkenes and alkynes during the evening rush hour was observed. The relationships between BC and VOCs, particularly with some specific VOCs species related to the neighboring chemical industrial park, demonstrated that the contribution of the surrounding chemical industrial parks to BC should not be neglected.

Short Circuit Protection: Challenges in the Early Years [History

During the evening rush hour of 24 July 1903, New York City's Manhattan Elevated Railway experienced a shutdown that began in its 74th Street power station and quickly involved the entire system. This station, one of the most advanced of its time, had been in operation for only seven months. It contained eight 5,000-kW, threephase, 25-Hz, 11,000-V alternators driven by reciprocating steam engines. Figure 1 shows one of these vertical steam engines with the large-diameter multipolar alternator attached. A frantic effort returned operation by late evening. However, just before midnight, another major system fault occurred. The trains were running again within 24 h, but the cause of the shutdown was unknown and resulted in much speculation in the engineering press.

Bypass Schemes of Urban Transit Station Based on Complex Network Theory

To study detour scheme after failure of urban transit station, urban transit network model is constructed by considering the reciprocal of urban transit line departure time interval as the weight. In accordance with the characteristics of the traffic flow, the network is studied under different time intervals, namely, morning, evening, and flat rush hours. First, an urban transit network topological characteristic index analysis model was constructed by combining ABC management method to identify the key elements of each indicator. Second, on the basis of the research on topological characteristic indicators, average path length and global efficiency of the network were used to measure the urban transit network connectivity, and an urban transit network station failure optimization scheme was designed. The optimization effect was judged by the changes in the two index values before and after the bus stop detour in accordance with the number of stations in the bypass area, namely, two stations, one station, and zero stations. Finally, the reliability of the model is studied, considering the urban transit network in Huangdao District of Qingdao. Through the analysis of node degree, second-order node degree, centrality, and other indicators, the urban transit network in Huangdao District of Qingdao was found to have evident small-world characteristics, and the top ranking stations of node degree value and centrality index are mainly distributed in the new urban edge planning area and the urban center area. In addition, for stations with the same rank, the node centrality value at each time interval has the following order: morning rush hours> evening rush hours> flat rush hours. The top 10 urban transit stations with node degree value are selected as the failure stations for optimization analysis. Results show that the average shortest path length of the optimized network decreases, the global efficiency of the network increases, and the optimized scheme is reliable.

Influence of Local Sources and Meteorological Parameters on the Spatial and Temporal Distribution of Ultrafine Particles in Augsburg, Germany

Ultrafine particles (UFP; diameter less than 100 nm) are ubiquitous in urban air, and an acknowledged risk to human health. At the same time, little is known about the immission situation at typical urban sites such as high-traffic roads, residential areas with a high amount of solid fuels for home heating or commercial and industrial areas due to missing legal requirements for measurements of UFP. Therefore, UFP were measured and evaluated in the (sub-)urban background as well as on spots influenced by these various anthropogenic local sources in the city of Augsburg, Germany, for the year 2017. In particular, the spatial and temporal correlations of the UFP concentrations between the seven measurement sites, the quantification and valuation of the contribution of local emitters with regard to their diurnal, weekly and seasonal variations and the influence of meteorological conditions on the formation and dispersion of UFP were investigated. Our analysis results demonstrate that urban UFP concentrations show a pronounced temporal and spatial variability. The mean concentration level of UFP varies between below 8,000 ultrafine particles/cm3at the suburban background site and above 16,700 ultrafine particles/cm3at the measurement station located next to a busy street canyon. At this particularly traffic-exposed measurement station, maximum concentrations of over 50,000 ultrafine particles/cm3were measured. The additional UFP load caused by intensive traffic volume during evening rush hour in connection with the unfavourable exchange processes in the street canyon can be quantified to concentrations of 14,000 ultrafine particles/cm3on average (compared to the immission situation of the urban background). An aggravating effect is brought about by inversion weather conditions in connection with air-polluted easterly winds, low wind speeds, lack of precipitation and very low mixing layer heights, such as over Augsburg at the end of January 2017, and cause peak concentrations of UFP.

MME-YOLO: Multi-Sensor Multi-Level Enhanced YOLO for Robust Vehicle Detection in Traffic Surveillance.

As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.

Source apportionment of black carbon aerosols from light absorption observation and source-oriented modeling: an implication in a coastal city in China

Abstract. Black carbon (BC) is the most important light-absorbing aerosol in the atmosphere. However, sources of atmospheric BC aerosols are largely uncertain, making it difficult to assess its influence on radiative forcing and climate change. In this study, year-round light-absorption observations were conducted during 2014 using an aethalometer in Xiamen, a coastal city in Southeast China. Source apportionment of BC was performed and temporal variations in BC sources were characterized based on both light absorption measurements and a source-oriented air quality model. The annual average concentrations of BC from fossil fuel (BCff) and biomass burning (BCbb) by the aethalometer method were 2932 ± 1444 ng m−3 and 1340 ± 542 ng m−3, contributing 66.7 % and 33.3 % to total BC, respectively. A sensitivity analysis was performed with different absorption Ångström exponent (AAE) values of fossil fuel combustion (αff) and biomass burning (αbb), suggesting that the aethalometer method was more sensitive to changes in αbb than αff. BCbb contribution exhibited a clear diurnal cycle, with the highest level (37.9 %) in the evening rush hour and a seasonal pattern with the maximum (39.9 %) in winter. Conditional probability function (CPF) analysis revealed the large biomass-burning contributions were accompanied by east-northeasterly and northerly winds. Backward trajectory indicated that air masses from North and East–Central China were associated with larger biomass-burning contributions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) suggested that North and East–Central China and Southeast Asia were potential sources of both BCff and BCbb. The source-oriented modeling results showed that transportation, residential and open biomass burning accounting for 45.3 %, 30.1 % and 17.6 % were the major BC sources. Among the three fuel catalogs, liquid fossil fuel (46.5 %) was the largest source, followed by biomass burning (32.6 %) and coal combustion (20.9 %). Source contributions of fossil fuel combustion and biomass burning identified by the source-oriented model were 67.4 % and 32.6 %, respectively, close to those obtained by the aethalometer method. The findings provide solid support for controlling fossil fuel sources to limit the impacts of BC on climate change and environmental degradation in the relatively clean region in China.

Research on the coupling degree of regional taxi demand and social development from the perspective of job–housing travels

Due to the urban industrial transformation and upgrading in China, the relationship between residential and employment spaces in large cities has changed significantly, resulting in an increasingly prominent job–housing separation phenomenon. Based on the taxi GPS trajectory data, this paper firstly analyzes the time and space distribution of job–housing–travel and the commuting characteristics of residents. Then, we construct the evaluation index systems of regional jobs–housing–taxi demand and regional social development level for Beijing. After calculating the weight of each index by using an entropy weight method, we analyze the coupling relationship between regional taxi demand and social development by building the coupling coordination degree model (CCDM). Conclusions are as follows: Firstly, it shows imbalance of space distribution of taxi orders in different administrative and functional areas. The order quantity gradually decreases from the main urban area to the edge, with the most in the living area, accounting for 46.23% of the total orders. Secondly, the daily variation trend of taxi orders in different functional areas is significantly different. During the morning rush hour, taxi orders flow out of residential areas and into office areas; however, during the evening rush hour, they flow out of the office and into the living area. For example, the residence area has a higher proportion of getting on passengers during the morning rush hour, which is 1.46 times that of the getting off ones. Thirdly, a low coupling coordination degree is observed between the regional social development level and the job–housing–taxi demand in Beijing, with a gradually decreasing from the main urban area to the outside, which is mainly caused by the imbalance between supply and demand. Therefore, it is suggested that the relevant government departments should formulate specific control schemes to traffic congestion according to local commuting features, and promote job–housing balance. Meanwhile, the taxi resources are supposed to be allocated reasonably by the striving to information symmetry, to promote the coordination and sustainability between the regional job–housing–taxi demand and social development level.

The Application of Lagrange Relaxation on Taxi Dispatchments During Evening Rush Hours

The city transportation status is directly related to the taxi supply arrangements during evening rush hours. The operation times, operation nature, operation requirements, vehicle resource types and quantities will have big influence on taxi drivers’ dispatchments, it is comparatively difficult to dispatch reasonably. This paper uses Lagrange Relaxation to solve the problem of taxi supply and demand during evening rush hours. NP-Hardness is the difficult point of dispatchments, thus Lagrange method is used to result an optimal solution on taxi dispatchments within reasonable calculating time, while the bottom limits of the approximate optimal solution and the approximate solution are resulted as well. This method can effectively solve the problem of the balance of taxi supply and demand during evening rush hours. Based on this, the dual problem is solved by using incremental subgradient method, while an improved strategy is proposed to rise the convergence rate.