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Data quality, involving the accuracy, completeness and reliability of data, is of great significance for the operation and management of road traffic. As the two significant factors that affect data accuracy, the market penetration rate (MPR) of CVs and the failure rate of roadside equipment (RSE) were considered in the heterogeneity traffic flow comprising human-driven vehicles and CVs. An optimal deployment method solved by SAGA was proposed to optimize the locations of RSE. A rigid nearest neighbor (RNN) algorithm and a soft nearest neighbor (SNN) algorithm were addressed to handle the missing data caused by sensor failure. Additionally, the BPNN algorithm was adopted to fuse RSE data and CV data. Case analysis results show that the proposed optimal deployment method is superior to the uniform and the hotspot methods. Data accuracy can reach 95% and 98% when the MPR is 15% and 60%, respectively. It decreases with the increase in sensor failure rate for single-source data, but not for the fused data. The performance of the SNN algorithm is better than the RNN algorithm in fixing single-source missing data. However, multi-source data fusion, especially with the high-precision data, is much more effective in improving data accuracy than missing data imputation.

Plug-in electric vehicles (PEVs) have been commercially available in the global market for about 3 years. Many countries have policies designed to stimulate consumer acceptance and accelerate market adoption. In the United States (U.S.), the biggest PEV market, sales have more than tripled since 2011. During the same period, PEV sales have increased, albeit slowly, in most western European countries. Notably, some European countries, such as Norway, showed strong increases mainly owing to generous incentives to PEV consumers. Japan is the second-largest PEV market in terms of number of vehicles sold. The Nissan battery electric vehicle (BEV) Leaf is the top-selling PEV model, with more than 100,000 units sold globally since its launch in 2010. In contrast, after 3 years of policy stimulation, PEV market share in China is still lower than 0.1 % of total car sales, and most of these vehicles were purchased by either central or local governments. However, PEV bus production in China has increased dramatically over last 3 years. These market trends, together with strong government policies, show that national and regional PEV-related incentives in selected countries can play an important role in jump-starting the PEV market.

Predicting the impact of electric vehicle (EV) fleet charging load on the grid load profile is essential for policymakers during grid planning. A systematic three-stage framework is proposed to forecast the long-term impact of EV home charging on national grids. The framework incorporates: (1) forecasting baseline grid load growth excluding EVs, (2) projecting EV market development, and (3) modeling EV charging behavior uncertainties (plug-in time & rate at home). For the first stage, five models (the autoregressive integrated moving average model, the artificial neural network model based on economic parameters, the nonlinear autoregressive exogenous neural network model, the long short-term memory network, and the convolutional neural network) are evaluated to select the most suitable model. The second stage is investigated using the Bass diffusion model in five penetration scenarios (10%-50%). The third stage is assessed using a probabilistic model based on data acquired by a public survey. The study applied Egypt as a case study, and the results are analyzed using peak load and load factor. Results revealed that 50% market penetration will increase peak load by 20.36% and reduce the load factor by 14.34% by 2040. However, the 10% market penetration limits these impacts to 3.16% and 2.46%, respectively. The study recommends applying demand-side management programs or controlling market expansion to balance the grid demand profile and EV adoption as policy implications. The framework is designed to accommodate a specific area, a city, or a country, as a scalable tool for policymakers addressing the energy-transport nexus in developing economies.

This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles’ market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate.

Purpose This paper aims to investigate the impact of a company’s decision to turn organic. Specifically, it examines the effect of such a decision on brand/product outcomes, and the role that organic market penetration plays in these effects. Design/methodology/approach Two experiments were conducted using two different food product categories. Data were analyzed using mean comparison tests, serial mediation and moderation analyses. Findings Results from Study 1 show that turning organic serially leads to increased perceptions of brand adaptability (mediator 1) and a positive effect on consumers’ perceived product quality (mediator 2), thus leading to stronger purchase intentions. Study 2 replicates and highlights the importance of market characteristics, showing that in markets with low organic market penetration rate (OMPR), brands turning organic are seen as challenging market norms, serially increasing (1) brand innovativeness (an additional mediator), (2) adaptability, (3) product quality perceptions, and then purchase intentions. Research limitations/implications This research used online experiments, but the analysis of actual consumer decisions would bring further insight into the effects of turning organic. Moreover, the experiments involved only food products, while other fast-growing organic product categories – like organic cosmetics – could be examined for replication purposes. Practical implications By turning organic, brands can position themselves as adaptable and responsive to changing market trends, which – in turn – positively influences how consumers perceive product quality. Companies can thus leverage this positioning by emphasizing their transition to organic in marketing campaigns, framing it as a response to evolving consumer values. Further, turning organic is more beneficial in markets with low OMPRs, which indicates that brands should consider turning organic primarily in such markets. Originality/value Unlike previous studies that focused on the static fact of being organic, this research adopts a dynamic view by showing that turning organic affects both product and brand outcomes. It also examines the specific market conditions under which turning organic is the most favorable for brands.

Rapid growth in vehicle ownership in the developing world and the evolution of transportation technologies have spurred a number of new challenges for policymakers. To address these challenges, this study develops a system dynamics (SD) model to project the future composition of Iran’s vehicle fleet, and to forecast fuel consumption and CO2 emissions through 2040. The model facilitates the exploration of system behaviors and the formulation of effective policies by equipping decision-makers with predictive insights. Under various scenarios, this study simulates the penetration of five distinct vehicle types, highlighting that an increase in fuel prices does not constitute a sustainable long-term intervention for reducing fuel consumption. Additionally, the model demonstrates that investments aimed at the rapid adoption of electric transportation technologies yield limited short-term reductions in CO2 emissions from transportation. The projections indicate that the number of vehicles in Iran is expected to surpass 30 million by 2040, with plug-in and hybrid electric vehicles (EVs and PHEVs) comprising up to approximately 2.2 million units in the base scenario. It is anticipated that annual gasoline consumption and CO2 emissions from passenger cars will escalate to 30,000 million liters and 77 million tons, respectively, over the next two decades. These findings highlight the need for a strategic approach in policy development to effectively manage the transition towards a lower-carbon vehicle fleet.

The field of Connected and Autonomous Vehicles (CAVs) promises to improve road safety through advanced communication technologies and data sharing. This study addresses the need for a clearer understanding of the relationship between CAV Market Penetration Rates (MPRs) and Safety Improvement Rates (SIRs) by conducting an updated meta-analysis that includes 49 studies published between 2015 and 2024. Across these studies, the primary focus of safety surrogate measures (SSMs) was on Time-to-Collision (TTC) and Post Encroachment Time (PET), reflecting their dominant role in evaluating traffic safety impacts of CAVs. We applied sensitivity analysis to refine the data and mitigate the effects of variability across simulation parameters and scenarios. A power function was identified as the most accurate model to describe the MPR-SIR relationship, capturing how safety benefits scale with increasing adoption of CAVs. At low MPRs (10%–20%), safety gains are modest, with SIRs of 2.8% and 4.9%, respectively. Mid-range MPRs (30%–60%), while frequently studied, show overestimated safety benefits in raw findings; our corrected estimates yield SIRs of 8.0% at 30% MPR and 9.4% at 40% MPR, highlighting the complexity of mixed traffic environments. At high MPRs (70%–90%), safety benefits become substantial and more consistent, with an SIR of 39.4% at 90% MPR. These findings inform transportation planners and policymakers on the safety potential of CAVs, emphasizing the importance of high market penetration to realize meaningful safety improvements.

The paper examines the entry behavior of producers in different industries in different export markets using a comprehensive data set of French firms. these data reveal enormous heterogeneity, primarily within industries, in the nature of market penetration. Nonetheless, some striking regularities appear both across and within industries. The findings that most firms do not export while those that do sell most of what they make at home suggest substantial barriers to exporting.

On the decision of whether to continue to implement the industrial support policy, two scenarios are set to predict the market penetration rate of China’s electric vehicles (EVs) (In this paper, the term Electric Vehicles (EVs) refers to both full-battery EVs and plug-in hybrids). In order to weaken the disturbance caused by international oil prices and industrial policies, the grey buffer operator was firstly applied, to preprocess the original data series. The sales data for EVs and fuel vehicles were buffered for second order and first order, respectively. Based on the obtained buffer data sequence, the GM (1, 1) model was used to predict the sales of EVs and fuel vehicles between 2022 and 2025 in China. The results demonstrate a significantly improved fit compared to directly modeling the raw data. This method is suitable for studying the market penetration rate prediction of China’s EVs. If the industry support policies continue (Scenario I), an EV market penetration rate of 22.45% can be achieved in 2024, and the expected target can be achieved one year ahead of schedule. Even if the corresponding industrial support policies are no longer implemented (Scenario II), the EV market penetration rate will reach 20.58% in 2025, and the set target of 20% will be achieved on schedule.

Traffic flow data, such as flow, density and speed, are crucial for transportation planning and traffic system operation. Recently, a novel traffic state estimating method was proposed using the distance to a leading vehicle measured by an advanced driver assistance system (ADAS) camera. This study examined the effect of an ADAS camera with enhanced capabilities on traffic state estimation using image-based vehicle identification technology. Considering the realistic distance error of the ADAS camera from the field experiment, a microscopic simulation model, VISSIM, was employed with multiple underlying parameters such as the number of lanes, traffic demand, the penetration rate of ADAS vehicles and the spatiotemporal range of the estimation area. Although the enhanced functions of the ADAS camera did not affect the accuracy of the traffic state estimates significantly, the ADAS camera can be used for traffic state estimation. Furthermore, the vehicle identification distance of the ADAS camera and traffic conditions with more lanes did not always ensure better accuracy of the estimates. Instead, it is recommended that transportation planners and traffic engineering practitioners carefully select the relevant parameters and their range to ensure a certain level of accuracy for traffic state estimates that suit their purposes.