Private Passenger Vehicles Research Articles

Comparative safety performance of autonomous- and human drivers: A real-world case study of the Waymo Driver

After several years of public road testing, the commercial deployment of fully autonomous vehicles—or Automated Driving Systems (ADS)—is poised to scale substantially following significant technological advancements and recent regulatory approvals. However, the fundamental question of whether an ADS is safer than its human counterparts remain largely unsolved due to several challenges in establishing an appropriate real-world safety comparison method. As scaling ensues, the lack of an established method can contribute to misinterpretations or uncertainties regarding ADS safety and impede the continuous and consistent assessment of ADS performance. This study introduces three research developments to define a robust and replicable safety comparison method to address this critical methodological gap. First, we introduce the use of liability insurance claims data to measure the comparative safety between ADS and human drivers. Second, we use Swiss Re insurance claims data to establish the first zip code- and responsibility-calibrated human performance benchmark, composed of over 600,000 private passenger vehicle claims and 125 billion miles of driving exposure. Third, we perform a case study by applying the developed baseline to evaluate the safety impact of the Waymo Driver. We find that when benchmarked against zip code-calibrated human baselines, the Waymo Driver significantly improves safety towards other road users. The comparison method established in this study can be replicated for other regions or ADS deployments to aid the decision-making of ADS safety stakeholders such as regulators, and instill trust in the general public.

Quantifying the Impact of Risk Factors on Direct Compensation Property Damage in Canadian Automobile Insurance

This study presents a statistical analysis assessing the impact of various risk factors on direct compensation property damage (DCPD) claims in private passenger vehicle accidents. Using automobile insurance data in Ontario, Canada for the decade years period between 2003 and 2012, a statistical model of property damage was explored via a generalized linear binary logit mixed model and considered the imbalance between the classes of insureds. The results indicate that several risk factors have a significant impact on the likelihood of DCPD claims, including usage, training, outstanding loss, and incurred loss. The effects of these risk factors were observed under the weights — the number of trials used to generate each success proportion — in the different classes of insureds. The generalized linear mixed models (GLMMs) analysis provides a powerful tool for quantifying the impact of risk factors on binary outcomes, which are called DCPD claims and property damage (PD) claims covered by third-party liability (TPL) insurance. These models can also inform insurance underwriting and policy design, focusing on identifying the most significant risk factors. The performance metrics calculated by considering the class imbalance in binary outcomes verify the resulting model’s ability to accurately predict classes. The F1 score, an evaluation metric to measure the performance of classification, was calculated as 0.934. In addition, PR AUC, which is the area under the Precision-Recall (PR) curve, was computed as 0.953. These high scores indicate that the resulting model performs well in the classification. The other metrics also support the classification accuracy of this model. The findings of the analysis can help insurers better understand the underlying drivers of property damages and develop more accurate and effective strategies for risk mitigation. Furthermore, this study highlights the importance of developing class-specific risk assessment models to account for the imbalance across different classes.

Integrating system dynamics and agent-based modeling: A data-driven framework for predicting electric vehicle market penetration and GHG emissions reduction under various incentives scenarios

As the growing deployment towards transportation electrification, a critical focus has emerged on quantifying the reduction contribution of greenhouse gas emissions from electric vehicles towards achieving carbon neutrality under diverse policy scenarios in the future. This necessitates a dynamic model that captures the evolving composition of the vehicle fleet and accurately forecasts the penetration and developmental trajectory of the electric vehicles in the car market. However, previous studies have largely overlooked the heterogeneity in user usage attributes, rendering them less effective in evaluating the impact of usage-based incentives on electric vehicle market penetration. To bridge this research gap, this study introduces an innovative, data-driven framework that integrates system dynamics and agent-based model. The proposed model can predict levels of electric vehicle penetration and corresponding greenhouse gas emission reductions within the private passenger vehicle sector, under a variety of policy scenarios. Our findings indicate that usage-based incentives, when implemented with optimal intensity, yield more significant emission reduction impacts and long-term economic benefits compared to conventional purchase-based subsidy. These insights not only furnish actionable policy suggestions to expedite the electric vehicle industry's growth in China but also offer valuable implications for other countries seeking to implement effective strategies for combating climate change and fostering sustainable transportation initiatives.

Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province.

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

A data-driven travel demand model to predict electric vehicle energy consumption: focusing on the rural demographic in the UK

ABSTRACT This paper presents a 7-day Travel Demand Model (TDM) for UK rural areas to aid the Electric Vehicle (EV) transition in these regions. Utilising data from both the UK Census Survey and UK National Travel Survey (NTS), private passenger vehicle travel patterns for a rural village in the Peak District National Park (UK), were modelled. This model is adaptable to any rural community within the UK, requiring only publicly available information on households and vehicles for that community. Using a novel approach through the development of lifestyle scenarios to understand the required household activities, the TDM incorporates five different trip purposes as the building blocks for a vehicle’s activity. Over a period of one week, 13,520 miles were driven by 84 vehicles across 49 households, that shows an EV fleet serving this community would consume 3562 kWh energy per week.

Regionally differentiated promotion of electric vehicles in China considering environmental and human health impacts

Private passenger vehicles, with its high emissions of CO2 and air pollutants, poses a severe threat to global climate and human health, particularly for a large developing country like China. Although both energy efficiency improvement of internal combustion engine vehicles (ICEVs) and the wide adoption of electric vehicles (EVs) could contribute to reducing emissions, how they should be jointly implemented in provinces with a heterogeneous context to maximize their net benefits remains insufficiently explored. Here, based on an integrated modeling framework associated with one factual (REF) and four counterfactual scenarios to explore the priority and best-ranked ordering of both EVs’ penetration and high energy-efficient ICEVs in 31 Chinese provinces to achieve the most environmental and human health benefits from 2011 to 2018. The results demonstrate that electrification of the passenger fleet, which is charged by a slightly cleaner power source relative to 2011, yields significant co-benefits of CO2 reduction and air quality improvement. Compared with REF, the fleet electrification scenario would lead to 3167 cases of avoided mortality and attain US$4.269 billion of health benefits in 2018, accounting for 0.03% of China’s gross domestic product. Nonetheless, highly efficient ICEVs are found to harbor decarbonization potential and health benefits in northern China. Based on these results, Sichuan, Hebei and seven other provinces in east China should promote EVs imminently; conversely, eight provinces with a high share of thermal power must continually advance their implementation of ICEVs in the near future. Such prioritization of EVs and ICEV development at the provincial level provides timely insights for devising tailored policies regarding passenger car transition and for maximizing climate and health benefits based on regional heterogeneity.

Estimating individual vehicle emission factors from near-road measurements in India

Over the years, vehicular emissions have been the main contributor to the deterioration of urban air quality. However, quantification of real-world vehicular emissions is quite limited in low- and middle-income countries like India. Developing real-world vehicle emission factors (EFs) using reference-grade instruments requires a significant amount of resources. This study aims to develop the individual and fleet vehicle EFs and the fraction of high-emitting vehicles using high-time resolution, low-cost sensors from near-road measurements – a first-of-its-kind study in India. Traffic and air pollutant measurements were conducted at the kerbside of a street canyon in Mumbai. The individual vehicle fuel-based EFCO and EFNOx were estimated using the plume-identification technique coupled with the information obtained from the vehicle registration number plates. The fleet mean (±SD) EFCO, EFNO, and EFNO2 were 6.30 (±3.16), 1.38 (±1.17), and 0.43 (±0.32) g/kg, respectively, while for EFPM1, EFPM2.5, EFPM2.5-10, and EFPM10 were 0.70 (±0.34), 1.19 (±0.57), 0.90 (±0.65), and 2.09 (±1.05) g/kg, respectively. The developed individual vehicle EFCO and EFNOx were greatly varied within each vehicle type due to differences in emission control technology, engine size, and the prevalence of “super-emitters”. There was no substantial difference in EFCO and EFNOx among the different BS emission standards across almost all vehicle types. The reconstructed fleet EFCO and EFNOx from the developed single-vehicle EFs were 1.4 and 1.9 times higher than the recorded fleet EFs. Approximately 14% of vehicles in the fleet were identified as super-emitters, responsible for 37–54% of total emissions, primarily from private passenger vehicles such as cars and two-wheelers. The EFCO and EFNOx from these high-emitters were 3–30 times greater than the laboratory-reported emissions. Our study suggests that improving emission standards alone is not enough to decrease tailpipe emissions from vehicles. Proper vehicle inspection and maintenance programs are crucial in controlling these emissions.

Can planned control measures for private passenger vehicles achieve China's carbon peak goal and mitigate the environmental impact?

There is a lack of consensus as to whether planned control measures for vehicular emissions can achieve China's carbon peak goal and mitigate the environmental impact of private passenger vehicles. This paper studies the environmental benefits of China's planned control measures by using a dynamic fleet-based life cycle assessment (LCA) method and “what-if” scenario analysis at a full national scale in a life-cycle systematic perspective. The results showed that burden-shifting may occur for electric vehicle (EV) deployment and demand-side interventions. China cannot achieve carbon peak goal before 2030 with the current planned control measures, unless the internal combustion engine vehicle (ICEV) fleet or the power grid is improved significantly. Large-scale deployment of EVs in the short term, with much improved ICEVs and a less clean power grid, is not conducive to peak fleet carbon emissions. Therefore, improving the ICEV fleet should be a priority for policy-making in the short term.

The Right Time for General Road Tax: Evidence from the Czech Republic

Road transport is a source of emissions of which nitrogen oxides (NOX), particulate matter (PM2.5), and carbon dioxide (CO2) emissions are most damaging. The economic inefficiency caused by not including the emission externalities of vehicle operation in prices can be improved by using a vehicle use tax. In the Czech Republic, however, a large part of the vehicle fleet is exempt from this tax, which is thus not general. This part of the fleet thus escapes taxation, for which there is no economic justification. To identify the actual ratio of emissions indicators of operating fleet segments in relation to vehicle use tax we use unique dataset composed of national emissions inventories data of the Copert program, road vehicle register data and vehicle testing stations data. It is also possible to quantify emissions production and determine the external costs of NOX, PM2.5, and CO2 generated by road transport segments in the Czech Republic. It was found that half of the production of the emissions in road transport in the Czech Republic representing almost 60% of the environmental costs were not covered by the existing tax due to the exclusion of part of the vehicle fleet from this taxation, especially private passenger vehicles.

Assessing Mumbai's in-use vehicular characteristics, current emissions, and future projections under various policy interventions

Vehicular emissions are the major source of air quality deterioration in Indian megacities. However, there is uncertainty in vehicular emission estimation due to the paucity of vehicular use and travel characteristics, and there is no specific methodology to assess the same. Thus, this study presents a methodology to capture the urban in-use vehicular characteristics. Additionally, it evaluates current vehicular emissions in Mumbai and estimates future emission levels for the year 2030, taking into account various policy interventions. Data for the study were collected via questionnaire surveys at fuel stations across Greater Mumbai – a first in western India. Exhaust and non-exhaust vehicular emissions were developed using the “bottom-up” methodology. Six scenarios were tested for exhaust vehicular emissions and energy consumption under various policy interventions. Monte-Carlo Simulations (MCS) were carried out to find the uncertainties in the vehicular emission estimation. Results showed that approximately 66% of the registered vehicles ply on Mumbai roads, and the on-road fuel efficiency is 12–33% less than the reported lab-based studies. Our study findings suggest that conducting surveys at three fuel stations is adequate for determining urban in-use vehicular characteristics with <5% bias. Reduction in vehicular emissions calls for stringent norms for private passenger vehicles and regulation of non-exhaust vehicular emissions. Given projected vehicular emissions for 2030, urban cities like Mumbai will have to inevitably replace conventional vehicles with electric vehicles to achieve the Paris agreement, which is to limit global warming well below 2 °C.

On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador

Emissions from mobile sources have become a major concern for health, environmental sustainability and climate change and high-resolution inventories are needed to support the design and assessment of abatement measures in urban areas. This study addresses the development of a traffic emissions inventory for Guayaquil, the second largest city in Ecuador, using the International Vehicle Emissions Model (IVE). Emissions are allocated with a spatial resolution of 1 km × 1 km and a temporal resolution of 1 h using a top-down methodology. This application combines traffic statistics already available in the city with the data from a field campaign to characterize vehicle fleet composition and activity patterns. The estimated annual emissions for the city were 237.1 kt of CO, 46.4 kt of NOx, 28.5 kt of VOC, 7.7 kt of PM10, 0.70 kt of SO2 and 4549.7 kt of CO2. 92.3 % of CO and 85.4 % of VOC were emitted by light gasoline vehicles, including private passenger vehicles and taxis, which represents 68.6 % and 8.8 %, respectively of the total fleet and contributes 52 % and 22 % of the total vehicle kilometer traveled (VKT), respectively. 48.9 % of NOx and 82 % of PM10 were emitted by the bus fleet although buses only represent 7.5 % of the total fleet and contribute 10.6 % of total VKT in the city. 41.1 % and 36.5 % of CO2 were emitted by buses and private vehicles, respectively. Even though, the average age of the fleet is below 10 years, the fleet in Guayaquil presents outdated emission standards and high emission factors. We found the higher emission rates in dense populated areas are associated to secondary roads. There is not much variability of emissions between months, but the typical daily pattern of emissions shows a peak in the morning and another in the afternoon.

Externality Assessment of Health Damage Caused by PM from PPV of China based on IPA

Health damage caused by pollutants is an important part of environmental externality. Its scientific and accurate evaluation is the basis of environmental policy-making and one of the recent research hotspots. In this paper, the Impact Pathway Approach (IPA) is used to evaluate the externality scale of health damage caused by Particulate Matter (PM) emitted by PPV (Private Passenger Vehicle) of China. Let the public, especially PPV owners, realize that the use of PPV has great health damage to others. Only by making active changes from themselves can reduce the negative externality. The core links of the IPA framework is the Concentration Response Function (CRF) and the determination of the average cost of each health damage observation point. In the end we got a total scale of health damage externality with the mean value of 2678522*104 yuan, in which premature death took the largest part.

Open source modelling of scenarios for a 100% renewable energy system in Barbados incorporating shore-to-ship power and electric vehicles

The high dependence on imported fuels and the potential for both climate change mitigation and economic diversification make Barbados' energy system particularly interesting for detailed transformation analysis. An open source energy system model is presented here for the analysis of a future Barbadian energy system. The model was applied in a scenario analysis, using a greenfield approach, to investigate cost-optimal and 100% renewable energy system configurations. Within the scenarios, the electrification of private passenger vehicles and cruise ships through shore-to-ship power supply was modelled to assess its impact on the energy system and the necessary investment in storage. Results show that for most scenarios of a system in 2030, a renewable energy share of over 80% is achieved in cost-optimal cases, even with a growing demand. The system's levelised costs of electricity range from 0.17 to 0.36 BBD/kWh in the cost-optimal scenarios and increase only moderately for 100% renewable systems. Under the reasonable assumption of decreasing photovoltaic investment costs, system costs of a 100% system may be lower than the current costs. The results show that pumped hydro-storage is a no-regret option for the Barbadian power system design. Overall, the results highlight the great potential of renewable energy as well as the technical and economic feasibility of a 100% renewable energy system for Barbados.

Using Crash Outcome Data Evaluation System (CODES) to examine injury in front vs. rear-seated infants and children involved in a motor vehicle crash in New York State

BackgroundIn New York State (NYS), motor vehicle (MV) injury to child passengers is a leading cause of hospitalization and emergency department (ED) visits in children aged 0–12 years. NYS laws require appropriate child restraints for ages 0–7 years and safety belts for ages 8 and up while traveling in a private passenger vehicle, but do not specify a seating position.MethodsFactors associated with injury in front-seated (n = 11,212) compared to rear-seated (n = 93,092) passengers aged 0–12 years were examined by age groups 0–3, 4–7 and 8–12 years using the 2012–2014 NYS Crash Outcome Data Evaluation System (CODES). CODES consists of Department of Motor Vehicle (DMV) crash reports linked to ED visits and hospitalizations. The front seat was row 1 and the rear rows 2–3. Vehicle towed from scene and air bag deployed were proxies for crash severity. Injury was dichotomized based on Maximum Abbreviated Injury Severity (MAIS) scores greater than zero. Multivariable logistic regression (odds ratios (OR) with 95% CI) was used to examine factors predictive of injury for the total population and for each age group.ResultsFront-seated children had more frequent injury than those rear-seated (8.46% vs. 4.92%, p < 0.0001). Children in child restraints experienced fewer medically-treated injuries compared to seat belted or unrestrained children (3.80, 6.50 and 13.62%, p < 0.0001 respectively). A higher proportion of children traveling with an unrestrained vs. restrained driver experienced injury (14.50% vs 5.26%, p < 0.0001). After controlling for crash severity, multivariable adjusted predictors of injury for children aged 0–12 years included riding in the front seat (1.20, 1.10–1.31), being unrestrained vs. child restraint (2.13, 1.73–2.62), being restrained in a seat belt vs. child restraint (1.20, 1.11–1.31), and traveling in a car vs. other vehicle type (1.21, 1.14–1.28). Similarly, protective factors included traveling with a restrained driver (0.61, 0.50–0.75), a driver aged < 25 years (0.91, 0.82–0.99), being an occupant of a later vehicle model year 2005–2008 (0.68, 0.53–0.89) or 2009–2015 (0.55, 0.42–0.71) compared to older model years (1970–1993).ConclusionsCompared to front-seated children, rear-seated children and children in age-appropriate restraints had lower adjusted odds of medically-treated injury.

Health and Climate Incentives for the Deployment of Cleaner On-Road Vehicle Technologies.

Reducing greenhouse gas (GHG) emissions of private passenger vehicles, transit buses, and commercial vehicles with newer technology can improve air quality, and, subsequently, population exposure and public health. For the Greater Toronto and Hamilton Area, we estimated the burden of each vehicle fleet on population health in the units of years of life lost and premature deaths. We then assessed the separate health benefits of electrifying private vehicles, transit buses, and replacing the oldest commercial vehicles with newer trucks. A complete deployment of electric passenger vehicles would lead to health benefits similar to replacing all trucks older than 8 years (i.e., about 300 premature deaths prevented) in the first year of implementation; however, GHG emissions would be mainly reduced with passenger fleet electrification. Transit bus electrification has similar health benefits as electrifying half of the passenger fleet (i.e., about 150 premature deaths prevented); however, the GHG emission reductions reached under the bus electrification scenario are lower by 90%. By accelerating policies to electrify cars and buses and renew older trucks, governments can save hundreds of lives per year and mitigate the impacts of climate change.

Private Vehicle Roadworthiness in Malaysia from the Vehicle Inspection Perspective

&#x0D; &#x0D; &#x0D; Vehicle defect is one of the contributing factors of road mishaps, although the magnitude of the problem is less prominent compared to human behaviour or road environment factors. What is more important is that this technical problem may find a more direct solution as opposed to human behavioural issues. This study aims to discuss common vehicle defects that probably contribute to road crashes by using Periodical Technical Inspection (PTI) database as the basis. Data was analysed to determine common failures of private passenger vehicles based on selected inspections and vehicle types. At this stage, only voluntary and routine inspections were scrutinized. In addition, this study is able to predict the probability of a vehicle failure by using information from the database. From such an analysis, it was found that the two most common private passenger vehicle defects were worn out tire (or lack of tread) and structural integrity. It was also found that vehicles sent for voluntary inspection have a higher probability of failure compared to those sent for routine inspection.&#x0D; &#x0D; &#x0D;

Identifying Roadway Physical Characteristics That Contribute to Emissions Differences between Hybrid and Conventional Vehicles

Conventional vehicles and hybrid electric vehicles (HEVs) will be the dominant vehicle types in the U.S. private passenger vehicle fleet for the foreseeable future. This study used a real-time data set obtained from monitoring vehicle performance and emissions from a series of test runs from a conventional vehicle and a HEV over an 18-month period. An innovative new method of analysis allowed the assessment of different emissions rates between the two propulsion systems and the attribution of these differences to physical roadway/infrastructure characteristics. Regression and cross-classification results identify specific roadway characteristics that contribute to emissions differences between the vehicle types. Overall, the models that included maximum grade and intersection control type performed best, however, speed limit and horizontal curvature were also shown to be important. The performance differences identified in this project confirm that engine controls that are responsive to roadway characteristics are necessary.

Energy and environmental benefits and policy implications for private passenger vehicles in an emerging metropolis of Southeast Asia – A case study of Metro Manila

An increase in private vehicle fleet and usage exacerbates the overuse of petroleum products, air quality degradation, and public health risks, among other drawbacks. This study investigated the energy and environmental benefits for private passenger vehicles under various scenarios in Metro Manila, the Philippines. The findings are informative to design policy implications for energy and environmental benefits. The study applied the Gaussian copula-based multinomial logit (MNL)-linear regression to develop the household vehicle ownership and energy consumption model using the disaggregated data of 1,795 households collected in 2017. The simulated vehicle fleet and energy consumption were then adopted to extrapolate energy demand, emissions, and public health risks for a 2010–2050 horizon using the bottom-up approach. Under the baseline scenario, the energy demand and carbon dioxide (CO2) emissions will reach 5.45 million tonnes of oil equivalent (Mtoe) and 16.93 million tonnes (Mt) in 2050, respectively. The amounts of hydrocarbon (HC), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOX), particulate matter 10 (PM10) and particulate matter 2.5 (PM2.5) emissions, and economic loss of health risks will be 6,214 tonnes (t), 62,136 t, 536 t, 10,550 t, 1,348 t, 996 t, and 237 Million USD, respectively, under the baseline scenario in 2050. Improvement of accessibility significantly reduces energy demand and CO2 emissions. Introduction of Euro 6 cars and Euro 4 buses and Public Utility Jeepneys noticeably improves public health. The new tax schedules on gas and vehicle maximize the tax revenue. The integration of all the mentioned alternatives can achieve sustainable mobility and urbanization.

Energy growth sources and future energy-saving potentials in passenger transportation sector in China

Abstract In China, energy use from passenger vehicles is increasing rapidly due to urbanization and income growth, but the contributions of transport service structure shift and energy efficiency improvement, especially future energy-saving directions are controversial and unclear because of the differences in the choices of traffic modes, which may greatly affect the analysis results and their interpretations. This study investigates the growth sources of energy use in the passenger transport sector during 1979–2017 and future energy-saving potentials by 2030 by re-examining statistical data. Results show that the transport service increase and transport structure shift led to the increase in energy use by 665.25% and 337.22%, but approximately 14% of the increased energy use was offset by energy efficiency improvements, and its effect is limited in public transportation sector, compared with private passenger vehicle sector. In public passenger transport sector, adjusting the service or volume structure may be a desirable option while in private passenger vehicle sector the policy focus is more on improving fuel efficiency and reducing average annual kilometers traveled per capita. Overall, there is still at least 28.3% of energy reduction potential by energy efficiency improvement before 2030 compared to 2017 baseline level. Potential evaluation showed that the current potential of transport service structure adjustment based on industrial planning is limited, which highlights the necessity of reform, involving promoting the sharing transportation, railway, subway and buses.

Spatial-temporal variability of PM2.5 air quality in Beijing, China during 2013–2018

This study investigates spatial-temporal variability and trends of ambient PM2.5 in Beijing, China, using data collected from eight urban and four suburban stations. During 2013–2018, the city-wide annual PM2.5 concentrations decreased significantly by 40% (84 μg/m3 in 2013 vs. 50 μg/m3 in 2018). The decreasing PM2.5 trend is more pronounced in winter and during the heating season (November–March), in urban areas, and at the median and upper percentiles of PM2.5 concentrations. The 95th percentile PM2.5 concentrations had decreased by 20 μg/m3/yr in the heating season and 16 μg/m3/yr in the non-heating season. During the six-year study period, there was a significant increase in excellent air quality days (PM2.5 concentration < 35 μg/m3) and a significant decrease in heavy pollution days (PM2.5 concentration > 150 μg/m3). PM2.5 concentrations were strongly correlated across the 12 stations. Urban areas in south Beijing experienced higher PM2.5 levels than suburban sites at every hour-of-day, day-of-week, and month-of-year. PM2.5 levels were higher during winter and the heating season, when PM2.5 emission was high due to space heating and mixing layer heights were low. PM2.5 was higher at weekends than during weekdays, when 20% of private passenger vehicles are prohibited, and higher at night than during the day, when heavy duty delivery vehicles are not permitted. These temporal and spatial trends suggest that Beijing's PM2.5 is strongly impacted by local emissions. Our results indicate, control strategies implemented were successful in Beijing's air quality improvement, but further reduction of PM2.5 concentrations in Beijing could be challenging due to significant contribution from its neighboring cities, calling for comprehensive and collaborative efforts in regional/national scale.