Search

BackgroundIn the treatment of Non-Small Cell Lung Cancer (NSCLC) the combination of Immuno- Oncotherapy (IO) and chemotherapy (CT) has been found to be superior to IO or CT alone for patients’ survival. Patients and clinicians are confronted with a preference sensitive choice between a more aggressive treatment with a greater negative effect on quality of life versus alternatives that are less effective but have fewer side effects.ObjectivesThe aims of this study were to: (a) quantify patients’ preferences for relevant attributes related to Immuno-Oncotherapy treatment alternatives, and (b) evaluate the maximum acceptable risk (MAR)/Minimum acceptable benefit (MAB) that patients would accept for treatment alternatives.MethodsAn online preference survey using discrete-choice experiment (DCE) was completed by NSCLC patients from two hospitals in Italy and Belgium. The survey asked patients’ preferences for five patient- relevant treatment attributes. The DCE was developed using a Bayesian D-efficient design. DCE analyses were performed using mixed logit models. Information regarding patient demographics, health literacy, locus of control, and quality of life was also collected.Results307 patients (158 Italian, 149 Belgian), stage I to IV, completed the survey. Patients preferred treatments with a higher 5-year survival chance as the most important attribute over all the other attributes. Preference heterogeneity for the attribute weights depended on health literacy, patients’ age and locus of control. Patients were willing to accept a substantially increased risks of developing side effects in exchange for the slightest increase (1%) in the chance of surviving at least 5 years from the diagnosis of cancer. Similarly, patients were willing to accept a switch in the mode of administration or complete loss of hair to obtain an increase in survival.ConclusionIn this study, the proportion of respondents who systematically preferred survival over all other treatment attributes was particularly high. Age, objective health literacy and locus of control accounted for heterogeneity in patients’ preferences. Evidence on how NSCLC patients trade between survival and other NSCLC attributes can support regulators and other stakeholders on assessing clinical trial evidence and protocols, based on patients’ conditions and socio-demographic parameters.

The fatal or incapacitating injury caused by overturn crashes involving sport utility vehicles (SUVs) is irreparable. The purpose of this study is to identify potential factors that affect the injury severity of overturn crashes involving SUVs and develop adequate preventive strategies. Given the unobserved heterogeneity existing in the data set, crash data in North Carolina from Highway Safety Information System (HSIS) is analyzed and separated by Latent Class Clustering into six relatively homogeneous groups. To further explore the heterogeneity, random parameter logit models are developed for each cluster, and the impacts of significant factors are estimated with marginal effects. The results reveal the heterogeneity across the clusters and the homogeneity within the same cluster. Variables (including females, people over fifty years old, improper or aggressive behavior, rural areas, high-speed limit, curved roadway, rolling and mountainous terrain, adverse weather, and poor light conditions) are associated with the injury severity of the overturn crashes involving SUVs. The findings of this study can further provide decision makers with insightful countermeasures to improve transportation safety and mitigate the injuries of overturn crashes involving SUVs. [ABSTRACT FROM AUTHOR]

The objective of this study is to identify and compare the contributing factors to pedestrian injury severity in pedestrian-vehicle crashes considering different land use patterns. The pedestrian-vehicle crash data from 2007 to 2018 were collected from the North Carolina Department of Transportation (NCDOT). A total number of 15,807 observations with 72 categorical variables were included in the final dataset. Two mixed logit models were developed to analyze the crash dataset with segmentations of two dominant land use areas (i.e., residential and commercial). Fixed and random parameters were found in both models. Estimation results and marginal effects of significant explanatory variables were investigated. In general, the residential model has 24 fixed parameters and 3 random parameters. The commercial model has 31 fixed parameters and 3 random parameters. According to the estimated results, elder or drunk factors are found to have more impacts on severe injuries in residential areas. Large and mid-size vehicles increase the probability of severe injuries in commercial areas. The marginal effect values for severe injury at non-intersections have opposite signs in the two models. Besides, speed limits between 40 and 45 mph and factors related to poor visibility are more likely to result in severe pedestrian injuries. Coarse asphalt pavement can reduce the probability of severe pedestrian injuries. This study investigated the pedestrian injury severity in pedestrian-vehicle crashes considering two types of land use using a mixed logit approach. Based on the discussions of factors contributing to the pedestrian injury severity, policies and countermeasures to improve traffic safety are suggested. Above all, a mixed-use land development policy is recommended. Other suggestions are summarized below: (1) giving more considerations to older pedestrians when planning and designing residential areas; (2) strengthening laws and education against drunk driving and even drunk walking on/across the roadways; (3) increasing the frequency of the patrols and alcohol tests; (4) improving lighting conditions and road alignments; (5) establishing a limited-truck-passing-period policy especially in commercial areas; and (6) improving the pavement conditions wherever needed. [ABSTRACT FROM AUTHOR]

Electric vehicle timeshare rentals (EVTRs) have been recognized as promising solutions to growingly severe problems of traffic congestion, air pollution, and insufficient parking spaces. This study aims to explore the factors that affect the adoption of EVTRs. To achieve the research objective, the household survey is conducted to obtain the travelers' attitudes towards their travel. Ten latent attitudinal factors are extracted based on the technology acceptance model (TAM) and the theory of planned behavior (TPB). The multi-index and multi-cause (MIMIC) method simultaneously estimates the correlations between the attitudinal factors. Two logit models with attitudinal factors or not are constructed to estimate the quantitative relationship between various factors and EVTR adoption. The results show that the accuracy of the mixed logit model with latent attitude variables is better than the binary logit model without attitude latent variables. This indicates that attitude latent factors could be well matched with the traveler's travel behavior and could better reflect travelers' travel demand. Perceived comfort, perceived efficient, subjective evaluation, use preference, and use willingness significantly impact EVTR use frequency. The inconvenience in travelers, rental stations, shared vehicles, and use modes have significant negative impacts. Finally, social pressure has no significant impact. Findings provide valuable insights regarding the efficient planning of the EVTR system and allow decision-makers to develop scientific and practical measures of EVTRs. [ABSTRACT FROM AUTHOR]

Traffic crashes involving pedestrians have a high frequency in developing countries. Among road users, pedestrians are the most vulnerable, as their involvement in traffic crashes is usually followed by severe and fatal injuries. This study aims to identify pedestrian crash patterns and reveal the random parameters in the dataset. A three-year (2015–2017) pedestrian crash dataset in Mashhad, Iran, was employed to investigate the influence of a rich set of factors on pedestrian injury severity, some of which have been less accounted for in previous studies (e.g., the vicinity to overpasses, the existence of vegetated buffers, and park lanes). A two-step method integrating latent class cluster analysis (LCA) and the mixed logit model was utilized to consider unobserved heterogeneity. The results demonstrated that various factors related to the pedestrian, vehicle, temporal, environmental, roadway, and built-environment characteristics are associated with pedestrian injuries. Furthermore, it was found that integrated use of LCA and mixed logit models can considerably reduce the unobserved heterogeneity and uncover the hidden effects influencing severity outcomes, leading to a more profound perception of pedestrian crash causation. The findings of this research can act as a helpful resource for implementing effective strategies by policymakers to reduce pedestrian casualties. [ABSTRACT FROM AUTHOR]

• Investigated factors affecting injury severity in single-vehicle run-off-road crashes for cars, SUVs, and pickups. • Developed a mixed logit model to analyze injury severity, accounting for variations across different vehicle types. • Significant factors include driver age, alcohol/drug use, seatbelt use, airbag deployment, and travel speeds. • Emphasized the importance of vehicle class-specific factors and recommended further research with larger datasets. This study aimed to identify and investigate the contributing factors influencing injury severity in single-vehicle run-off-road (ROR) crashes, which are known for their high severity. The primary objective was to analyze and compare the impact of these factors across three distinct vehicle classes: passenger cars, sport utility vehicles (SUVs), and pickups. A mixed logit model with heterogeneity in mean and variance was developed to analyze the injury severity outcomes in ROR crashes for the three vehicle classes. The model accounted for the potential variations in the impact of contributing factors across different vehicle types. The study revealed several significant variables consistently influencing injury severity across all three vehicle classes. These included driver age, alcohol or drug usage, seatbelt utilization, airbag deployment, higher travel speeds, and the vehicle model year post-2010. Notably, as driver age increased, the impact on changes in injury severity outcomes was more pronounced for pickup drivers compared to those operating passenger cars and SUVs. Among the common findings was the highly effective role of seatbelt usage in mitigating injury severity in ROR crashes. Additionally, passenger cars were associated with increased injury severity, particularly at relatively higher travel speeds exceeding 75 mph when contrasted with SUVs and pickups traveling between 61 and 75 mph. The study highlights the importance of considering vehicle class-specific factors in analyzing injury severity in ROR crashes. Recommendations include further in-depth investigations into distinct factors contributing to injury severity within each vehicle class and utilizing more extensive crash datasets to gain additional insights for enhancing road safety. [ABSTRACT FROM AUTHOR]

In the rapidly advancing field of automated driving systems, the automated vehicle subscription (AVS) can provide the easiness of trialing the latest automated vehicle (AV) technologies at a monthly cost and gather significant customer interest. However, a gap in the literature remains regarding attitudes and willingness to pay (WTP) towards this innovative business model AVS, which is an alternative to the widely studied Self-owned AV and shared AV (SAV). Thus, we conducted a stated preference (SP) survey among the car-oriented population in China to assess the willingness to pay for the automated vehicle subscription, investigating its potential among the non-automated vehicle, Self-owned AV, and SAV. In the context of level 4 AVs, mixed logit model results reveal that automated vehicle subscription stands out among all AV alternatives, with all respondents demonstrating a positive willingness to pay for the automated vehicle subscription. In comparison, respectively 96 % and 71 % of the respondents have a positive willingness to pay for the Self-owned AV and SAV. Meanwhile, compared to a non-automated vehicle with a fixed ownership cost of 300 K CNY (1CNY = 0.14US$) and assuming a 10-CNY trip cost for the most frequent trip, the median willingness to pay of an AV is (1) 3 K CNY/month for the automated vehicle subscription; (2) an extra 130 K CNY for a Self-owned AV; (3) 418 CNY/month for an SAV. Furthermore, the purpose of travel, income, and individual attitudes toward automated driving significantly influence the willingness to pay for the automated vehicle subscription. Notably, individuals traveling for business purpose and belonging to the high-income group show a stronger inclination towards the automated vehicle subscription. Overall, automated vehicle subscription has a high willingness to pay and is shown to be a promising path forward in AV adoption. [ABSTRACT FROM AUTHOR]

Roundabout crashes are common worldwide but have received little attention. In particular, the investigation remains relatively understudied in distinct collision modes of roundabout crashes, including angle and non-angle crashes. This study investigates differences in factors affecting injury severity between angle and non-angle crashes and how these differences change over time. Random parameter logit models with heterogeneity in mean and variance were estimated using 2012–2019 Florida crash data. Variables considered for the modeling include temporal characteristics, environmental characteristics, road characteristics, spatial characteristics, vehicle characteristics, crash characteristics, and human characteristics. In addition, likelihood ratio tests were used to investigate the temporal instabilities of the models and differences in collision modes. The results showed that only a few variables demonstrated temporally stable effects for angle collisions (weekend and dark-lighted) and non-angle collisions (paved shoulders and cyclists), respectively. Unique influencing factors of injury severity were identified for different collision modes, such as dark-lighted, straight alignment, merge, lane departure, the disabled driver for angle collisions, and clear, fog, commercial vehicles, and aggressive driving for non-angle collisions. The results of the out-of-sample prediction simulations further demonstrate the difference in injury severity over time for angle and non-angle collisions. Overall results suggest that countermeasures can be implemented to reduce the injury severity of angle crashes/non-angle crashes based on the significant variables identified. This study may be used to improve roundabout safety by considering different collision modes.

There is low LARC uptake in Nigeria and not much is documented about Nigerian women's preferences for these products and how these preferences affect women's choices. Therefore, the purpose of this study is to provide empirical evidence on the decision-making behavior of Nigerian women regarding LARCs. Three hypothetical LARCs were presented. Informed by results obtained from the review of relevant literature and focus group discussion held with selected family planning services providers, the identified attributes were effect on weight, cost, LARC effectiveness and effect on bleeding patterns, each at three levels. A d-optimal design was used to construct nine choice sets. An additional choice set was included to conduct the test-retest stability test. The choice sets and other socio-demographic questions formed the discrete choice experiment questionnaire. A random sample of 150 eligible women was selected from women who enrolled and attended family planning clinics in public healthcare facilities in Ibadan, Nigeria through a three-stage sampling technique. Mixed logit modeling was used to obtain estimates of the parameters and willingness to pay (WTP) for each attribute was calculated. The test-retest stability result showed that 76.7% of the respondents responded in a consistent and rational manner. The women preferred LARCs that have no effect on their weights compared to LARCs that cause either slight weight loss or gain. Also, they preferred LARCs that offer 1/1000 level of effectiveness in a year. The most preferred attribute level, ‘women experience infrequent bleeding’, increases WTP by NGN 13,984.584 (USD 33.68) while the least preferred level, ‘women experience frequent/prolonged bleeding’, has a negative mean WTP (-NGN 19,598.378 (-USD 47.20)) indicating dispreference for the level. Furthermore, women derived higher utilities from very cheap LARCs. Evidence-based information on women's preferences regarding LARC usage would help policymakers in the deployment of strategies that would ensure that preferred LARCs are procured and made readily available to women.

Price differentiation is a common strategy in many markets. In this paper, we study a static multiproduct price optimization problem with demand given by a discrete mixed multinomial logit model. By considering a mixed logit model that includes customer specific variables and parameters in the utility specification, our pricing problem reflects well the discrete choice models used in practice. To solve this pricing problem, we design an efficient iterative optimization algorithm that asymptotically converges to the optimal solution. To this end, a linear optimization (LO) problem is formulated, based on the trust-region approach, to find a “good” feasible solution and approximate the problem from below. A convex optimization problem is designed using a convexification technique to approximate the optimization problem from above. Then, using a branching method, we tighten the optimality gap. The effectiveness of our algorithm is illustrated on several cases, and compared against solvers and existing state-of-the-art methods in the literature.

The micromobility mode of transportation is convenient for short-distance trips in urban settings. The popularity of dockless sharing systems is rapidly increasing because of their characteristics of convenient rental/return and mobile payment options. Although many previous studies have examined travel behaviors regarding a single means of micromobility transportation, few have analyzed mode choice behavior among multiple, similar, and competing means of micromobility transportation. To our knowledge, no researcher thus far has examined micromobility as a potential means to solve the last-mile problem from the subway station to the destination. Therefore, a stated preference survey was conducted, analyzing factors and preferences influencing the mode choice of subway users for the last-mile trip. This study considered sharing dockless e-scooters and docked bicycles, as well as walking. It analyzed the differences in mode choice behavior of subway users based on their demographic and socio-economic attributes, as well as travel (travel purpose and distance), mode-specific (fare), and environmental (slope) characteristics. Two logit-based models—the multinomial logit (MNL) and mixed logit (ML) models—and two ML models based on residential areas of Seoul and Gyeonggi, Korea, were developed and compared. The results showed that the ML model was superior to the MNL model regarding model fit and individual parameters. In the ML model, the statistically significant variables included fare, marital status, household size, age, income, disability, and slope of roadway. However, the significance differed by alternative modes: for e-scooters (vs. bicycles), the factors influencing last-mile mode choice behaviors showed greater statistical significance. A comparison of the two ML models by region highlighted major differences in the results for the variables of household size and trip purpose between residents of Seoul and Gyeonggi. Urban transport policymakers interested in introducing micromobility services can use the findings of this study to make more well-informed decisions about traffic congestion problems and increase public transportation revenue in an environmentally friendly manner.

This study investigates the relationship between existing environmental and non-environmental landscapes and residents' preferences for landscapes in their neighborhoods. Investigating this relationship contributes to expanding the knowledge on the spatial heterogeneity in people's environmental preferences. Spatial heterogeneity in people’s environmental preferences is related to the economic evaluation of environmental policies and the efficient provision of environmental amenities. We conducted a discrete choice experiment on 579 residents in Ishikawa Prefecture, Japan. The survey data were combined with geographic information system (GIS) data on land use and analyzed as a spatial structured data of 1 km2 using a mixed logit model. The main empirical finding is that the willingness to pay for a marginal increase in the surrounding landscape generally increases with the abundance of the surrounding landscape. We also found that the increase in the willingness to pay depends on the type of environmental and non-environmental landscape. Moreover, there was no relationship between the existing landscape and the preference for abandoned environmental landscapes. This study highlights the spatial sorting phenomenon in residential choice, which is a source of spatial heterogeneity in environmental preferences. When conducting environmental evaluation, it is necessary to explicitly incorporate the spatial heterogeneity in environmental preferences into any analytical framework.

To introduce the garbage class mixed logit (MIXL) model as a convenient alternative to manually screening and accounting for respondents with low data quality in discrete choice experiments. Garbage classes are typically used in latent class logit analyses to designate or identify group(s) of respondents with low data quality. Yet, the same concept can be applied to MIXL models as well. Based on a reanalysis of 4 discrete choice experiments that were originally analyzed using a standard MIXL model, it is shown that garbage class MIXL models can achieve the same effect as manually screening for (and excluding) respondents with low data quality based on the more commonly used root likelihood test, but with less effort and ambiguity. Including a garbage class in MIXL models removes the influence of respondents with a random choice pattern from the MIXL model estimates, provides an estimate of the number of low-quality respondents in the dataset, and avoids having to manually screen for respondents with low data quality based on internal or statistical validity tests. Although less versatile than the combination of standard MIXL estimates with separate assessments of data quality and sensitivity analyses, the proposed garbage class MIXL model provides an attractive alternative. [ABSTRACT FROM AUTHOR]

The significance of large trucks for the expansion and well-being of the economy is a well-established fact. However, crashes involving large trucks significantly threaten the overall safety on the roads. Moreover, a significant proportion of fatal crashes involving large trucks occurs on interstate roadways in the United States. However, not many studies have focused on the heterogeneous effects of the contributory factors on injury outcomes of interstate crashes involving large trucks. The current study explores the application of a k-prototypes clustering-based mixed logit model to identify and analyze the heterogeneous effects of contributory factors on injury outcomes in different scenarios of interstate crashes involving large trucks. Data from six years of crashes involving large trucks that occurred on interstate roadways in the state of Pennsylvania, US, were used in this study. The list of contributory factors included the following: drivers' demographics and behaviors; crash characteristics; vehicle-related factors; location and roadway attributes; and environmental factors. The results indicated that some of the contributory factors were significant for all scenarios of interstate crashes involving large trucks. However, the magnitude of those factors' effects varied across scenarios. Moreover, some of the contributory factors were exclusive to certain scenarios of interstate crashes involving large trucks. Lastly, the identification of random parameters in the cluster-based models indicated that a cluster-based mixed logit model is a more effective approach for accurately estimating the effects of contributory factors on injury outcomes in large-truck interstate crashes. The empirical findings of this study can be used to develop more robust traffic laws and safety measures to reduce the frequency and severity of injury in different scenarios of interstate crashes involving large trucks. [ABSTRACT FROM AUTHOR]

Vulnerable road users (VRUs) including pedestrians and cyclists tend to experience more severe injuries when they are involved in crashes compared with motorized vehicle users. Such concern has been expressed as an impediment to the promotion of environment-friendly transportation. To provide insights on the causes of crashes involving VRUs, this study aims to explore the underlying factors that contribute to VRUs injury severity levels and provide constructive recommendations to mitigate injury severity in crashes. In order to minimize heterogeneity existing in the collected data, a latent class clustering method is conducted to categorize collected crash records into different groups. Then the mixed logit models are developed for each cluster as well as the overall crash data. The analysis is conducted based on the crash data retrieved from the Highway Safety Information System (HSIS) from 2012 to 2016 in North Carolina. Distinguished sets of significant factors are identified for clusters with different dominant features. Some factors are found to yield different or even opposite effects in identified clusters, including male gender and non-roadway location. These findings would enhance the understanding of the vulnerable road user (VRU) injury severity mechanism and help policymakers to make reasoned and efficient decisions to improve safety. [ABSTRACT FROM AUTHOR]

One common limitation faced in mode choice modeling is data imbalance. Mode choice models, such as logit models, may output biased estimations for alternatives with smaller shares and consequently have high prediction errors. Since accurate prediction of the less commonly used modes is important in some applications, such as predicting transit mode share in many auto-oriented American cities, it is essential to improve the prediction capability of logit models for those modes. Hence, this study applies an imbalanced learning technique and evaluates the prediction capability and interpretability of logit models under both balanced and imbalanced datasets using a case study for the City of Nashville, Tennessee. The results show that the proposed method improves the accuracy of the less commonly used modes and the mean absolute percentage error by 18% and 2%, respectively, while keeping the models interpretable. Finally, we provide some high-level guidelines for mode choice modeling with imbalanced data. [ABSTRACT FROM AUTHOR]

The Florida Department of Transportation (FDOT) has recently started implementing a new signal system at mid-blocks called Mid-block Pedestrian Signals (MPS). This study aims to evaluate the effectiveness of these newly implemented MPSs. A total of 260 h of video data were collected from five locations across Florida, with 130 h recorded before MPS installation and 130 h after installation, including both weekdays and weekends. State-of-the-art computer vision technology was employed to detect and track various road users. A random parameters multinomial logit model with heterogeneity in the means was implemented to assess safety of vehicle-pedestrian interaction by three conflict categories: No Conflict, Moderate Conflict, and Serious Conflict. Relative-Time-to-Collision (RTTC) values were utilized to classify these level of conflicts. The analysis demonstrates that the presence of MPS significantly enhances safety outcomes by increasing the likelihood of avoiding conflicts and reducing the probabilities of both moderate and serious conflicts. Key factors influencing conflict probabilities were identified, including pedestrian and vehicle counts, average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix, all of which increase the probability of serious conflicts. Interestingly, the analysis identified three significant interaction variables with MPS: average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix. While these factors individually had a higher probability of leading to serious conflicts, the presence of MPS effectively mitigates these risks by moderating their adverse effects, increasing the likelihood of no conflicts. These results underscore the importance of MPS as an effective measure to improve safety at mid-block crossings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Several important vaccines, such as the Haemophilus influenzae type b vaccine, rotavirus vaccine, pneumococcal conjugate vaccine, and influenza vaccine, have not been included in China's National Immunization Program (NIP) due to a prolonged absence of updates and limited resources. Public engagement could identify concerns that require attention and foster trust to ensure continuous support for immunization., Objective: This study aimed to identify public preferences for vaccine inclusion in the NIP and to determine the desired vaccine funding priorities in the Chinese population., Methods: A dual-response discrete choice experiment was utilized to estimate the relative importance of 6 attributes, including incidence of vaccine-preventable diseases, mortality of vaccine-preventable diseases, vaccine effectiveness, vaccine cost, vaccinated group, and vaccine coverage. Participants were recruited through the Wenjuanxing platform using a census-based quota sample of the nationwide population aged 18 years and older. A mixed logit model was used to estimate the coefficient of attribute preferences and predict the selection probability. Subgroups and interaction effects were analyzed to examine the heterogeneity in preferences., Results: In total, 1258 participants completed the survey, of which 880 were involved in the main analysis and 1166 in the sensitivity analysis. The relative importance and model estimates of 2 attributes, vaccine cost and vaccination group, varied between the unforced- and forced-choice settings. All 6 vaccine attributes significantly influenced the preferences for vaccine inclusion, with vaccine effectiveness and coverage as the most important factors, followed by the vaccinated group and mortality of vaccine-preventable diseases in the unforced-choice settings. The top vaccines recommended for China's NIP included the varicella vaccine, Haemophilus influenzae type b vaccine, enterovirus 71 vaccine, and influenza vaccine for preschoolers and school-aged children. The current analysis also revealed distinct preference patterns among different subgroups, such as gender, age, education, and income. The interaction analysis indicated that the region and health status of participants contribute to preference heterogeneity., Conclusions: Public preferences for including vaccines in the NIP were primarily influenced by vaccine effectiveness and coverage. The varicella vaccine should be prioritized for inclusion in the NIP. The public preferences could provide valuable insights when incorporating new vaccines in the NIP., (© Lingli Zhang, Xin Li, Jiali Chen, Xiaoye Wang, Yuyang Sun. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

BackgroundChina is the only country in the world that has not included the Haemophilus influenzae type b (Hib) vaccine in its National Immunization Program, making it more difficult to eliminate Hib-related diseases through Hib vaccination. It is necessary to study parental preferences for Hib vaccination to optimize vaccine promotion strategies in China.ObjectiveThis study aimed to investigate Chinese parental preference for five attributes of Hib vaccination, i.e., the place of origin, effectiveness, adverse event, doctors' recommendation, and the price of full vaccination when making a decision to vaccinate their children under 2 years old.MethodsA cross-sectional survey was conducted in two cities in Zhejiang Province from November to December in 2020 using a discrete choice experiment (DCE). A mixed logit model was used to estimate participating parents' preference for Hib vaccination attributes included in the DCE. Subgroup analysis and probability analysis were also conducted to capture the heterogeneity and trade-off of parental preference for Hib vaccination.ResultsData from 6,168 observations were included in the analyses. Parents of children are, on average, more likely to voice a positive preference for Hib vaccination. Such attributes of Hib vaccination as effectiveness and doctor's recommendation have a significant positive influence on parents' preference for Hib vaccination, while imported vaccines, adverse events, and the price of full vaccination have a significant negative influence on parents' preference. Parents with different demographic characteristics also existed heterogeneities in preference for Hib vaccination. Parents will make a trade-off on price if the Hib vaccine has a good performance on effectiveness and safety.ConclusionThe study found that, regardless of the place of origin of the Hib vaccine, parents with children under 2 years old prefer to compromise on price if the vaccine has a better effectiveness and safety profile. A proactive recommendation from doctors would strengthen their willingness for Hib vaccination. These findings help aid the development of communication strategies with parents for Hib vaccination in China.

The severity of the two-vehicle crash is closely related to the characteristics of both the struck and striking vehicles. Ignoring vehicle roles may lead to biased results. Thus, this study used mixed logit models to determine the factors that influence injury severity in the two-vehicle crash, taking into account the vehicle characteristics of the different crash roles. The data used is collected from Pennsylvania Department of Transportation (PennDOT) Open Data Portal. First, the synthetic minority oversampling technique and nearest neighbors (SMOTE-ENN) strategy was selected to address the class imbalance problem of crash data. Then, two separated mixed logit models were developed for four- and three-legged unsignalized intersections. The results suggest that the type and movement of vehicles have significant effects on crash severity. For example, right-turn vehicles being struck can lead to more serious crashes than striking other vehicles. Large trucks striking other vehicles are found to increase crash severity, but being struck is found to decrease crash severity. Additionally, several factors were also identified to affect crash severity in both models and effective countermeasures suggestions were proposed to mitigate crash severity. Supplemental data for this article is available online at at. [ABSTRACT FROM AUTHOR]

This study investigates the effect of the exchange rate on foreign direct investment (FDI). The exchange rate effect manifests itself theoretically but remains ambiguous empirically. In the literature, (a) FDI is treated predominantly as a continuous variable, while the discrete nature of FDI decisions is neglected; (b) FDI data are in aggregated form, masking the true firm‐level exchange‐rate–FDI relationship; and (c) regression models adopted are ill‐suited to analyse exchange rate and FDI data as both of these display different time‐series properties. To overcome these problems, we propose the use of (i) a discrete choice model with a mixed logit application to capture discrete investment decisions and firm heterogeneity; (ii) cross‐section dimension to reduce the problem arising from the time‐series dimension; and (iii) firm‐level location choice data and real exchange rate data reflecting international price level differences so that (i) and (ii) are implemented. With these new features, we perform an empirical analysis using firm‐level data on the location choice of Chinese outward FDI during 2005–2016. Our estimation results detect a significant exchange rate effect not accounted for previously, explaining why the evidence for the effect has been mixed across studies. [ABSTRACT FROM AUTHOR]

Pedestrian injury has become a national traffic-safety concern as the share of pedestrian fatality continues to increase in the last decade. Pedestrian injury severities are influenced by many factors that include driver, pedestrian, vehicle, roadway, temporal, and environmental characteristics. Results indicate that some of the factors affecting pedestrian injury severity at intersection and non-intersection locations are statistically different and using the same model to perform the estimate at both locations may result in biased results. However, few studies have been conducted to explore different contributing factors at such locations. Mixed logit models are developed to independently identify the contributing factors to pedestrian injury severity resulting from crashes at intersections and non-intersections. The estimation shows factors such as male driver, alcohol, pedestrian above 65, truck, and higher speed limit significantly increase the probability of pedestrian serious injury severities in both locations. However, the impacts tend to be more severe at intersections. Urban and wet road surfaces decrease the likelihood of suffering fatal injury at intersections. Furthermore, crash time only has impacts at intersections, while traffic control, severe weather, and day-of-week only have impacts at non-intersections. The results provide insights on developing more effective countermeasures to promote pedestrian safety. [ABSTRACT FROM AUTHOR]

As the private sector is under heavy pressure to serve the ever-growing e-commerce market, the potential of implementing new disruptive mobility/logistics services for increasing the level of the current last-mile delivery (LMD) services, is emerging. Vehicle automation technology, characterized by high-capacity utilization and asset intensity, appears to be a prominent response to easing this pressure, while contributing to mitigation of the adverse effects associated with the deployment of LMD activities. This research studied the perceptions of Greek end-users/consumers, regarding the introduction of autonomous/automated/driverless vehicles (AVs) in innovative delivery services. To achieve this, a mixed logit model was developed, based on a Stated Preferences (SP) experiment, designed to capture the demand of alternative last-mile delivery modes/services, such as drones, pods, and autonomous vans, compared to traditional delivery services. The results show that the traditional delivery, i.e., having a dedicated delivery person who picks up the parcels at a consolidation point and delivers them directly to the recipients while driving a non-autonomous vehicle—conventional van, bike, e-bike, e-scooter—remains the most acceptable delivery method. Moreover, the analysis indicated that there is no interest yet in deploying home deliveries with drones or AVs, and that participants are unwilling to pay extra charges for having access to more advanced last-mile delivery modes/services. Thus, it is important to promote the benefits of innovative modes and services for LMD, in order to increase public awareness and receptivity in Greece. [ABSTRACT FROM AUTHOR]

This paper uses a multi-profile best-worst scaling dataset to compare the mixed logit model and the latent class logit model for mobile payment choice. Three non-nested tests are applied to show the comparison results. The results indicate that the mixed logit model is superior to the latent class logit model in all three tests. [ABSTRACT FROM AUTHOR]

Looking ahead to the future-stage autonomous transportation system (ATS), personal mobility service (PMS) aims to provide the recommended travel options based on both microscopic individual travel demand and the macroscopic supply system objectives. Such a goal relies on massive heterogeneous data to interpret and predict user travel intentions, facing the challenges caused by prevalent centralized approaches, such as an unbalanced utilization rate between cloud and edge, and data privacy. To fill the gap, we propose a federated logit model (FMXL), for estimating user preferences, which integrates a discrete choice model—the mixed logit model (MXL), with a novel decentralized learning paradigm—federated learning (FL). FMXL supports PMS by (1) respectively performing local and global estimation at the client and server to optimize the load, (2) collaboratively approximating the posterior of the standard mixed logit model through a continuous interaction mechanism, and (3) flexibly configuring two specific global estimation methods (sampling and aggregation) to accommodate different estimation scenarios. Moreover, the predicted rates of FMXL are about 10% higher compared to a flat logit model in both static and dynamic estimation. Meanwhile, the estimation time has been reduced by about 40% compared to a centralized MXL model. Our model can not only protect user privacy and improve the utilization of edge resources but also significantly improve the accuracy and timeliness of recommendations, thus enhancing the performance of PMS in ATS. [ABSTRACT FROM AUTHOR]

This article examines a crucial question that has been raised in recent years in both policy and economic literature: the role of uncertainty as a barrier to energy retrofit decisions. We develop a discrete choice experiment to elicit preferences for energy renovation measures. This methodologically innovative experiment design includes two insurance schemes covering potential sources of uncertainty as attributes of the energy retrofit alternatives. We use a mixed logit model to investigate the nature of systematic heterogeneity in household preferences for attributes of energy retrofit solutions. The article shows that uncertainties related to future energy pricing and retrofit quality are negatively perceived during energy-renovation decision making. This impact varies according to household characteristics such as risk aversion and perceptions of the economic context. The results suggest that public policies should support and accompany the development of insurance schemes to increase energy renovation rates. [ABSTRACT FROM AUTHOR]

• Evaluated the effectiveness of newly implemented Mid-block Pedestrian Signals (MPS) using extensive video data. • Utilized advanced computer vision technology to detect and track various road users with precision. • Applied Relative-Time-to-Collision (RTTC) values to classify and assess different levels of pedestrian-vehicle conflicts. • Implemented a random parameters multinomial logit model with heterogeneity in means to analyze safety outcomes. • Found that MPS significantly enhances pedestrian safety, proving to be highly effective in supporting safe crossings. The Florida Department of Transportation (FDOT) has recently started implementing a new signal system at mid-blocks called Mid-block Pedestrian Signals (MPS). This study aims to evaluate the effectiveness of these newly implemented MPSs. A total of 260 h of video data were collected from five locations across Florida, with 130 h recorded before MPS installation and 130 h after installation, including both weekdays and weekends. State-of-the-art computer vision technology was employed to detect and track various road users. A random parameters multinomial logit model with heterogeneity in the means was implemented to assess safety of vehicle–pedestrian interaction by three conflict categories: No Conflict, Moderate Conflict, and Serious Conflict. Relative-Time-to-Collision (RTTC) values were utilized to classify these level of conflicts. The analysis demonstrates that the presence of MPS significantly enhances safety outcomes by increasing the likelihood of avoiding conflicts and reducing the probabilities of both moderate and serious conflicts. Key factors influencing conflict probabilities were identified, including pedestrian and vehicle counts, average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix, all of which increase the probability of serious conflicts. Interestingly, the analysis identified three significant interaction variables with MPS: average leading vehicle speed, standard deviation of leading vehicle speeds, and land-use mix. While these factors individually had a higher probability of leading to serious conflicts, the presence of MPS effectively mitigates these risks by moderating their adverse effects, increasing the likelihood of no conflicts. These results underscore the importance of MPS as an effective measure to improve safety at mid-block crossings. [ABSTRACT FROM AUTHOR]

This paper investigates two fundamental questions related to the new development of high-speed rail (HSR) infrastructure in Indonesia: (1) What are the variables that affect intercity traveler preference between private cars via toll roads, intercity trains, and HSR? and (2) what are the determinants of psychological intentions for intercity travelers regarding HSR? On the basis of a survey of 412 Bandung–Jakarta travelers, the mixed logit model and a modification of the theory of planned behavior were deployed to analyze the extent to which the mode choice of travelers was affected by various factors, including trip characteristics, sociodemographic, and travelers’ perceptions on HSR. The analyses reveal that existing car users tend to keep using their cars via toll roads. The demand for HSR comes from existing intercity train users with high-income levels, whereas train users with lower income levels prefer to continue using intercity trains. With regard to other sociodemographic effects, females and travelers younger than 50 years old would be more likely to switch to HSR. Travelers with business trips are also more likely to use HSR. Furthermore, the study found that the use intention of HSR is more influenced by subjective norms, perceived moral obligation, and perceived behavioral controls rather than attitude variables such as comfort and reliability. Several policy recommendations are outlined from the findings to achieve the sustainability of HSR in the future.

Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively; thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19.

• The K-means algorithm is employed to determine diversion patterns. • The LSTM algorithm is employed to construct the personalized traffic conflict prediction model. • Mixed logit models are used to explore the causal mechanisms of traffic conflict using pre-conflict condition data extracted from diversion vehicles. • A new lane line reconstruction method is proposed to mitigate measurement errors. Lane change behavior disrupts traffic flow and increases the potential for traffic conflicts, especially on expressway weaving segments. Focusing on the diversion process, this study incorporating individual driving patterns into conflict prediction and causation analysis can help develop individualized intervention measures to avoid risky diversion behaviors. First, to minimize measurement errors, this study introduces a lane line reconstruction method. Second, several unsupervised clustering methods, including k-means, agglomerative clustering, gaussian mixture, and spectral clustering, are applied to explore diversion patterns. Moreover, machine learning methods, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Attention-based LSTM, eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), are employed for real-time traffic conflict prediction. Finally, mixed logit models are developed using pre-conflict condition data to investigate the causal mechanisms of traffic conflicts. The results indicate that the K-means algorithm with four clusters exhibits the highest Calinski-Harabasz and Silhouette scores and the lowest Davies-Bouldin scores. With superior classification accuracy and generalization ability, the LSTM is used to develop the personalized traffic conflict prediction model. Sensitivity analysis indicates that incorporating the diversion patterns into the LSTM model results in an improvement of 3.64% in Accuracy, 7.15% in Precision, and 1.34% in Recall. Results from the four mixed logit models indicate significant differences in factors contributing to traffic conflicts within each diversion pattern. For instance, increasing the speed difference between the target vehicle and the right preceding vehicle benefits traffic conflict during acceleration diversions but decreases the likelihood of traffic conflicts during deceleration diversions. These results can help traffic engineers propose individualized solutions to reduce unsafe diversion behavior. [ABSTRACT FROM AUTHOR]

• Temporal stability in factors affecting injury severity in large truck crashes was explored by conducting a four-year analysis to reveal patterns and variations over time. • Consistent and stable variables, across multiple years were identified, offering detailed insights into the persistent contributors to large truck crash injury severities. • The article provides targeted policy recommendations based on identified stable factors, empowering stakeholders with specific strategies to address injury severity in large truck-involved crashes and enhance overall road safety. An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses. [ABSTRACT FROM AUTHOR]

• Five years (2015–2019) of New Jersey crashes involving cellphone were used. • A mix logit model was used to analyze the distracted driving crashes. • Higher speed limits increased crash severity while higher annual average daily traffic reduces the crash severity. Introduction: Distracted driving is a concern for traffic safety in the 21st century, and can be held responsible for the increasing propensity and severity of traffic crashes. With the advent of mobile technologies, distractions involving the use of cellphones while driving have emerged, and young drivers in particular are getting more and more engaged in these distractions. Texting or receiving phone calls while driving are offenses in most states, and they are punished with fiscal penalties. Awareness campaigns have also been arranged over recent decades across the United States in order to minimize crashes due to distracted driving. The severity of such crashes depends on driver behavior, which can also be affected by various factors like the geometric design of the roadway, lighting and environmental conditions, and temporal variables. Method: In this study, we analyzed data on five years (2015–2019) of crashes involving cellphone use in New Jersey using a mixed logit model. As estimated model parameters can vary randomly across roadway segments in this approach, this allowed us to account for unobserved heterogeneities relating to roadway characteristics, environmental factors, and driver behavior. A pseudo-elasticity analysis was further employed to observe the sensitivity of the significant explanatory variables to crash severity. Results: We found that higher speed limits and a larger total number of vehicles involved both increased crash severity, while higher annual average daily traffic (AADT) levels and the presence of an urban road setting reduced it. Practical Applications: These findings will help decision-makers to comprehend what the significant contributing factors associated with crash injury severity due to distracted driving are, and how to implement necessary interventions to reduce this severity. [ABSTRACT FROM AUTHOR]

In transportation, pedestrians are among the most vulnerable entities. Each year, a total of about 2,000 pedestrians are reported to be involved in traffic crashes with vehicles in North Carolina. Research efforts are needed to identify influencing factors and develop safety improvement measures for pedestrians. This study applies mixed logit (ML) model approach to exploring the potential unobserved heterogeneities across individual injury observations. Factors that significantly contribute to pedestrian injury severities resulting from pedestrian-vehicle crashes are examined under a variety of categories, including motorist, pedestrian, environmental, and roadway (etc.) characteristics. Police reported pedestrian-vehicle crash data collected from 2007 to 2014 in North Carolina are utilized. Parameter estimates and associated elasticities are used to interpret the results. [ABSTRACT FROM AUTHOR]

As one of the most frequently occurring crashes, rear-end crashes often result in injuries and property damage, especially when large trucks are involved. To investigate the contributing factors and the unobserved heterogeneity in such factors, a mixed logit model is developed to analyze rear-end crashes involving large trucks. A dataset containing 7,976 rear-end crashes involving large trucks is collected from Highway Safety Information System (HSIS) in North Carolina between 2005 and 2013. Driver, roadway, and environmental related characteristics are considered in the analysis. Speed limit over 50 mph is found to be better modeled as a random-parameter at specific injury severity levels. Results also indicate that driving under the influence of alcohol or drugs, rural roadways, dark light condition, grade roadway configuration, speed limit over 50 mph will significantly increase the injury severity of large truck involved rear-end crashes. Roadway with traffic control will significantly decrease the injury severity of such crashes. The findings in this study can greatly help traffic agencies and truck companies develop better large truck-involved rear-end crash prevention strategies. [ABSTRACT FROM AUTHOR]

This study analyzes consumer preferences in South Korea for alternative fuel vehicles (AFVs), including battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs), instead of conventional fuel vehicles. A survey targeting 1500 potential car buyers in three years was conducted wherein the subjects stated their preferences depending on the varying conditions of AFV attributes and charger accessibility. Cluster-based multinomial logit and mixed logit models were developed to identify influential factors affecting consumer preferences. The models incorporated the sociodemographic characteristics of users, attitudinal perceptions, and vehicle attributes to capture their interactive impacts. The results of the estimated models suggest that a reduction in purchase price can substantially boost AFV sales, particularly those of HFCVs, with a direct elasticity of 1.78. Additionally, the models demonstrated that attitudinal perceptions, such as perceived environmental and economic benefits are significant factors. Moreover, potential car buyers who plan to buy one vehicle within one year showed the least preference for purchasing BEVs, indicating the importance of technology maturity in the BEV market. These findings can provide reasonable guidelines for establishing marketing strategies and stronger support to achieve the targeted market penetration of AFVs in a city or country. [ABSTRACT FROM AUTHOR]

As a part of the shift towards a more sustainable society, improving the energy efficiency of the current dwelling stock has gained momentum in the past few years. In case of co-ownership of a building (e.g. apartment building), the residents of a home-owner association have to come to a collective agreement when deciding upon the implementation of sustainable energy measures of the dwellings. Stated choice modelling is a well-known technique to measure individuals' preferences of attributes of choice alternatives. This study introduces an extension of the technique to estimate preferences of residents that takes into account group influences and group dynamics in joint decision-making. The findings of an application based on a national sample of 167 home-owner associations members indicate that owner association members show cooperative behaviour. They are willing to make concessions to their own preferences in order to reach a group decision. When preferences of members differ on outcomes that are considered to be very important, however, the willingness to adapt may be limited. We conclude therefore that compromise alternatives have a higher probability of acceptance than high-gain high-cost alternatives, given the group decision context. [ABSTRACT FROM AUTHOR]

At signalized intersections, red-light running crashes can lead to serious injuries or deaths. Red-light running is significantly influenced by intersection factors and human factors. To test driver response to circular yellow and circular red indications, an experiment was performed using a driving simulator. Data included 1,272 observations from 53 participants with a red-light running violation rate of 3.7%. A study was conducted to examine whether drivers decided to run a red light when a circular red light was displayed at signalized intersections while being closely followed. Participants' heterogeneity was accounted for using a mixed logit model.The study found that drivers ran red lights more frequently when they had a longer time to reach the intersection and a shorter tailway. No statistically significant difference was found between the following vehicle classifications and red-light violations. Additionally, results of the mixed logit model did not find a significant connection between gender of drivers and their driving behavior approaching signalized intersections. All red-light running violations occurred when the time to intersections was either 4.5 s or 5.5 s. About 60% of red-light violations exceed the posted speed limit of 45 mph, running the red light with an average speed of 47 mph. Furthermore, 59% of red-light running violations occurred between 1-1.5 s after the circular red signal commenced.

To overcome the environmental side effects of over-utilizing fossil fuels and road traffic emissions, Alternative Fuel Vehicles (AFVs) are alternative power engines. However, as AFVs have not yet penetrated the automotive market in Iran, using the choice experiment model, this study considers the heterogeneous preference for Electric Vehicles (EVs) in four megacities of Iran. Therefore, 1000 respondents are interviewed and are asked to choose their preferred vehicle between hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), and internal combustion engine vehicles. Furthermore, this study contributes to the literature by employing the Policoric correlation matrix, not Pearson, to investigate the influence of psychological factors besides the macro, micro, and product factors in adopting Evs. The latent class and mixed logit model reveal the monetary factors negatively affected consumers' willingness to pay for HEVs and BEVs. In contrast, the charging availability and non-monetary policies are positively connected to the acceptance rate of BEVs, While risk-aversion and normative-face perspectives reduce it. Furthermore, the class membership variable shows that the younger tend to purchase HEVs, and high-income groups are the most significant potential buyers of HEVs and BEVs. Therefore, the younger and high-income groups could be considered the target group in Iran's early penetrating EVs. [ABSTRACT FROM AUTHOR]

The need to recognise and account for the influence of working from home on commuting activity has never been so real as a result of the COVID-19 pandemic. Not only does this change the performance of the transport network, it also means that the way in which transport modellers and planners use models estimated on a typical weekday of travel and expand it up to the week and the year must be questioned and appropriately revised to adjust for the quantum of working from home. Although teleworking is not a new phenomenon, what is new is the ferocity by which it has been imposed on individuals throughout the world, and the expectation that working from home is no longer a temporary phenomenon but one that is likely to continue to some non-marginal extent given its acceptance and revealed preferences from both many employees and employ where working from home makes good sense. This paper formalises the relationship between working from home and commuting by day of the week and time of day for two large metropolitan areas in Australia, Brisbane and Sydney, using a mixed logit choice model, identifying the influences on such choices together with a mapping model between the probability of working from home and socioeconomic and other contextual influences that are commonly used in strategic transport models to predict demand for various modes by location. The findings, based on Wave 3 (approximately 6 months from the initial outbreak of the pandemic) of an ongoing data collection exercise, provide the first formal evidence for Australia in enabling transport planners to adjust their predicted modal shares and overall modal travel activity for the presence of working from home. [ABSTRACT FROM AUTHOR]

Truck-related crashes in the work zones always resulted in severe injury outcomes. This paper analyses the injury severity of truck-involved crashes in the work zones based on a ten-year (2005–2014) data collected in North Carolina. A wide range of variables is considered, including the driver, environmental, work-zone, road, and vehicle characteristics. The mixed logit (MXL) and partial proportional odds (PPO) logit models are employed to investigate the potential unobserved heterogeneity associated with the indicator of urban and rural highways. A comparison of the two models shows that PPO outperforms the MXL model. The estimation results showed that contributing factors resulting in injury-severity levels of work zones crashes involving trucks on rural and urban highways are significantly different. Elasticity effects are provided and used to evaluate the significance of the variables in the rural and urban models. The findings of this study help understand the injury-severity outcomes in truck-involved crashes in the rural and urban work zones. [ABSTRACT FROM AUTHOR]

Eco-environmental protection of river basins and compensation for damages have been important issues for researchers around the world for a long time. Many studies have focused on the correlations among individual socioeconomic characteristics, ecological cognition, and differences in the willingness to pay. However, no research has been conducted from the perspective of perceived environmental quality. According to the Broken Windows Theory, the public’s willingness and behaviors regarding environmental protection are determined largely by earlier perceptions of environmental quality. Therefore, we used a spatial choice experiment to investigate the willingness of the public to pay for ecosystem restoration in the upper, middle, and lower reaches of the Xijiang River Basin in China. This paper discusses if perceived environmental quality is a factor that creates different levels in the willingness to pay. Our results show that the Broken Window Effect can better explain these differences. Living in a better ecological environment, the upper-reaches public expect to pay for the restoration of the river basin’s ecosystem to a higher state and is willing to be the “first person” to repair the “broken windows,” whereas those in the middle and lower reaches are willing to pay only for a restoration to a good state.

Existing studies have not analyzed travelers' travel modal shift behavior after bus fare adjustment for special urban topography. In order to fill this gap and explore the strategies that can effectively encourage the shift of valley city travelers to rail transit after the adjustment of bus fare, the price adjustment perception and topographic space perception are introduced to expand the theory of planned behavior (TPB). On this basis, an integrated model combining structural equation model (SEM) and Mixed Logit model (MLM) is established to analyze the factors of travelers' modal shift behavior of in the valley city after bus fare adjustment, and elastic analysis is carried out. Taking Lanzhou as an example, questionnaire data and traffic data were collected for example analysis. The results show that fare adjustment perception (FAP) and topographic space perception (TSP) have the same significant impact on travel modal shift intention (TMSI) and behavior (TMSB) as subjective norms (SN), shift behavior attitude (SBA) and perceived behavior control (PBC), and the travel characteristics and psychological perception sensitivity of travelers in the valley city are heterogeneous. After the adjustment of bus fares in valley city, the shift to rail transit is the most. When the subjective norms, fare adjustment perception and topographic space perception of travelers in the valley city increase by 50 %. The sharing rate of rail transit is increased by 10.284 %. The effective way to increase the sharing rate of rail transit is to increase the combined factors of subjective norms, topographic space perception and fare adjustment perception. Compared with the Multinomial Logit model and the Logit model combined with structural equation model, the goodness of fit and prediction accuracy of the proposed integrated model are improved., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

The effect of vehicle transmission type on driver injury severities have not been thoroughly studied. The study used four-year historical crash data that occurred between the year 2019 and 2022 in Ghana. The data shows 1856 and 2272 crashes for automatic and manual transmission, respectively. The study examined the factors influencing driver injury severity in crashes involving vehicles with manual and automatic transmissions, using Random Parameter Mixed Logit Model to account for heterogeneity in the dataset. It was observed that use of manual transmission is related to a higher risk of incapacitating and fatal injuries compared to automatic transmission. Specifically, for automatic transmission vehicle-involved crashes, factors related to fatal injury were overaged vehicles, public transport, morning and evening peak hours, head-on and rollover crashes. Crashes involving saloon cars and low age cars were associated with incapacitating injury whiles rainy weather condition was related to both fatal and incapacitant injuries. Regarding manual transmission, fatal injury was associated with crashes involving male and novice drivers, cars, pickup trucks, HGV, public transports, morning and evening peak hours, rainy weather conditions and curved roads. Also, buses, private cars and trip distance were related to incapacitating injury. The rollover crashes and overaged vehicles were also associated with both fatal and incapacitating injuries. Four random parameters demonstrated heterogeneity in means, with two factors influencing the variances of two parameters for automatic transmission model. For the manual transmission model, five random parameters showed heterogeneity in means, with four variables influencing the variances of three parameters. These findings are valuable for policymakers, manufacturers, and drivers in implementing targeted interventions and safety measures to promote road safety., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author.)

Background: The incidence of herpes zoster (HZ) is rapidly increasing, causing both clinical and economic burdens in China. Very little is known about Chinese residents' HZ vaccine preferences and willingness to pay (WTP) for each vaccination attribute., Objective: This study aims to elicit the preferences of Chinese urban adults (aged 25 years or older) regarding HZ vaccination programs and to calculate WTP for each vaccination attribute., Methods: In this study, we interviewed 2864 residents in 9 cities in China. A discrete choice experiment was conducted to investigate the residents' preferences for HZ vaccination and to predict the uptake rate for different vaccine scenarios. A mixed logit model was used to estimate the preferences and WTP for each attribute. Seven attributes with different levels were included in the experiment, and we divided the coefficients of other attributes by the coefficient of price to measure WTP., Results: Vaccine effectiveness, protection duration, risk of side effects, place of origin, and cost were proven to influence Chinese adults' preferences for HZ vaccination. The effectiveness of the HZ vaccine was the attribute that had the most predominant impact on residents' preferences, followed by protection duration. The residents were willing to pay CN ¥974 (US $145) to increase the vaccine effectiveness from 45% to 90%, and they would barely pay to exchange the vaccination schedule from 2 doses to 1 dose. It is suggested that the expected uptake could be promoted the most (by 20.84%) with an increase in the protection rate from 45% to 90%., Conclusions: Chinese urban adults made trade-offs between vaccine effectiveness, protection duration, place of origin, side effects, and cost of HZ vaccination. Vaccine effectiveness was the most important characteristic. The residents have the highest WTP (CN ¥974; US $145) for enhancing the effectiveness of vaccines. To maximize HZ vaccine uptake, health authorities should promote vaccine effectiveness., (©Yiqi Xia, Ming Wang, Mingzheng Hu, Yanshang Wang, Beibei Yuan, Dawei Zhu, Ping He. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 09.08.2024.)

The objective of this research is to identify and compare contributing factors to pedestrian injury severities in pedestrian-vehicle crashes considering both time-of-day and day-of-week. The pedestrian-vehicle crash data are collected from 2007 to 2018 in North Carolina with categorical factors of pedestrian, driver, vehicle type, crash group, geography, environment, and traffic control characteristics. The final dataset includes 17,904 observations with 69 categorized variables. Four mixed logit models are developed to analyze the crash dataset with segmentations of weekday daytime, weekday nighttime, weekend daytime, and weekend nighttime. A total number of 31 fixed significant factors and 6 random parameter factors to the pedestrian injury severity are detected in four mixed logit models. According to marginal effects, large vehicle involved, pedestrians with age over 65, hit and run, drunk pedestrian, down/dusk light, dark without roadside light, and industrial land use are identified as the contributing factors that result in more than a 0.08 increase in the probability of fatal injury. Compared to the daytime, most factors are found to have more impact on severe injuries in the nighttime. Also, most factors are found to result in more severe injuries on weekends than on weekdays. This study identifies and compares the factors to pedestrian injury severity in pedestrian-vehicle crashes considering the temporal variance in time-of-day (i.e., daytime vs. nighttime) and day-of-week (i.e., weekdays vs. weekends). Random effects are explored in mixed logit models. Differences and possible reasons for the significant factors' impact within and across time-of-day and day-of-week are also investigated. Corresponding countermeasures and suggestions to mitigate the impacts of major factors are also discussed, which give practical guidance to planners and engineers, and provide a solid reference to further explore the temporal variance of the crash data. [ABSTRACT FROM AUTHOR]