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Bicycling has been actively promoted as a clean and efficient mode of commute. Besides, due to the personal and societal benefits it provides, it has been adopted by many city dwellers for short-distance trips. Despite the integral role this active transport mode plays, it is unfortunately associated with a high risk of fatalities in the event of a traffic crash as they are not protected. Many studies have been conducted in several jurisdictions to examine the factors contributing to crashes involving these vulnerable road users. In the case of Louisiana which is currently experiencing increased cases of severe and fatal bicycle-involved crashes, less attention has been paid to investigating the critical factors influencing bicyclist injury severity outcomes using more detailed data and advanced econometric modeling frameworks to help propose adequate policies to improve the safety of riders. Against this background, this study examined the key contributing factors influencing bicyclist injuries by using more detailed roadway crash data spanning 2010–2016 obtained from the state of Louisiana. The study then applies an advanced random parameter logit modeling with heterogeneity in means and variances to address the unobserved heterogeneity issue associated with traffic crash data. To overcome the imbalanced data issue, three major crash injury levels were used instead of the conventional five crash injury levels. Besides, the data groups classified under each injury level were compared for the final variable selection. The study found that distracted drivers, elderly bicyclists, careless operations, and riding in dark conditions increase the probability of having severe injuries in vehicle-bicyclist crashes. Moreover, the variables for straight-level roadways and city streets decrease the odds of severe injuries. The straight-level roadway may provide better sight distance for both drivers and bicyclists, and complex environments like city streets discourage crashes with severe injuries.

Copyright of Journal of Southeast University (English Edition) is the property of Journal of Southeast University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Speed and crash data were compared before and after the onset of the pandemic on low-volume rural freeways in Michigan. • Changes in general risk-taking behavior of the drivers was also compared for crash-involved drivers. • The pandemic was shown to increase travel speeds, as well as the severity of traffic crashes. • Speed and safety trends reverted back towards pre-pandemic levels at sites where speed limits were increased but remained elevated at sites where speed limits did not increase. • Crash involved drivers in 2020 were more likely to not wear seat belts, drive under the influence of drugs or alcohol, drive when they were sick or fatigued, or involve in hit-and-run crash. The COVID-19 pandemic disrupted various aspects of day-to-day life in unprecedented ways. This included significant reductions in travel that corresponded with travel restrictions imposed to contain the spread of the virus. This study evaluated changes in driver speed selection and crash/injury risk over the course of the pandemic on rural freeways in Michigan. Daily speed data and monthly crash data were analyzed from 2015 to 2020 to investigate changes that corresponded to the onset of the COVID-19 pandemic. The results showed that during the initial periods of lockdown, mean speeds increased by 2.2–2.4 mph across all sites, while the total crash frequency was lower by 28%–45% compared to pre-pandemic years. Crashes also tended to be significantly more severe during the early stages of the pandemic. Interestingly, these changes were less pronounced on sites with 70-mph speed limits as compared to sites where speed limits were increased to 75 mph in 2017. However, as travel restrictions were lifted, speeds regressed towards long-term averages on the 75-mph sites while the higher speeds were maintained at sites with 70-mph limits. The increase in injury severity appears to be attributable to higher speeds, as well as increases in other high-risk behaviors during the pandemic, such as non-use of seat belts and driving under the influence of drugs or alcohol. Some of these effects are also due to differences in terms of the population of drivers that were on the road during the pandemic. The results provide important insights as to the increases in severe crashes that coincided with the onset of the pandemic. [ABSTRACT FROM AUTHOR]

Smallholder farmers' decisions to adopt improved varieties are expected to be critically governed by their preferences and willingness to pay for different traits of these varieties. This study examined farmers' preferences for sorghum (Sorghum bicolor (L.) Moench) variety attributes and estimated their willingness to pay (WTP) for these attributes using choice experiment data from >1,300 sorghum farmers in Tanzania. Empirical findings showed that farmers had strong preferences for sorghum varieties that were tolerant to environmental stresses, high yielding, early maturing, fetching higher grain prices, and white in color. Significant heterogeneity was observed in farmers' preferences across various traits. The WTP estimates revealed that farmers were willing to pay the highest premium for tolerance to environmental stresses, amounting, on average, to three times higher than the WTP for other traits. Our results have important implications for demand-driven variety development that could contribute to improving crop productivity and household welfare. [ABSTRACT FROM AUTHOR]

On mountainous freeways, some tunnels are located adjacent to each other, composing a tunnel group where the safety conditions are more challenging compared to a single tunnel. However, most of the existing studies have focused on single tunnel safety, and the research efforts to investigate traffic safety, especially the injury-severity analysis of the tunnel group crashes, is scarce. Therefore, the present study employed a random parameter logit model to examine the factors affecting the injury severity of the freeway tunnel group crashes. The analysis is based on a five-year of police-reported data set of 377 crashes collected from six tunnel groups in Hunan Province, China. The results indicate that the daytime, weekdays, entrance zone, downgrades, elder drivers, speeding, fatigue driving, and rollover collisions are positively associated, while winter, curves, and sideswipes are negatively associated with severe crashes and have signs consistent with engineering intuition. More importantly, due to the complex driving environment of the tunnel groups, the summer, access zone, connecting zone, and drivers with less driving experience increases the probability of severe crashes. Also, the effects of the access zone, elderly drivers, speeding, and sideswipe collisions were found to be best modeled with random parameters in this study. Multiple countermeasures are provided to improve tunnel groups traffic safety, including the provision of variable message signs to provide information to the drivers regarding the speeding limits and distance to the tunnel, periodic maintenance of the illumination according to the lighting guidelines in the tunnel groups, implementation of the automatic section speed control for speeding, and public awareness about the complex driving environment of the tunnel groups. [ABSTRACT FROM AUTHOR]

Most studies that address the integration of active modes (i.e., walking and bicycling) and public transit tend to focus on either transfer distance or feeder mode choice. Few articles have reported the relationships between the built environment (D-variables) and transfer patterns (e.g., transfer distance and mode choice) in a single study. Furthermore, only very few studies accounted for travelers' judgements of walkability and/or bikeability and distinguished between access and egress trips. This paper fills these gaps in the literature using data collected in Nanjing, China. A random parameter Tobit and a random parameter multinomial logistic model are estimated to investigate respectively (i) the association of perceived walkability/bikeability and features of the built environment in metro catchment areas on walking/bicycling distance of access/egress trips and (ii) the probability of choosing active modes as transfer modes. The modeling results reveal that perceived walkability/bikeability and features of the built environment are more significantly correlated with mode choice behavior than with transfer distance. Moreover, walking/bicycling distance and access/egress mode choice tend to be strongly associated with perceived walkability and bikeability as well as with features of the built environment. The results are helpful in deepening urban planners and policymakers' understanding of how to design built environments that maximize the integration of active modes and metro. [ABSTRACT FROM AUTHOR]

Effective evacuation management plans can help reduce the negative impacts of disasters. Understanding evacuee travel behavior is critical for the design of evacuation plans. In this paper, we explore which factors contribute to evacuees selecting freeway vs. non-freeway evacuation routes. Freeways are of particular interest due to their ability to evacuate large volumes of traffic. This study used survey data collected for the Hampton Roads region of Virginia. Respondents were asked to provide their preferred route types in the event of a hypothetical Category 4 hurricane evacuation. A mixed (random parameters) logit model was proposed to determine factors that influence evacuees selecting between freeway and non-freeway route. The study found that several factors contribute to evacuees choosing a freeway over other routes. In the descending order of importance (i.e., marginal effects), these factors are: willingness to use the official recommended route, living in a single-family or duplex housing, expected travel time to reach the destination, being employed, and possessing prior evacuation experience. Conversely, a few factors had a negative effect on choosing a freeway. These factors are: willingness to evacuate two days prior to landfall and evacuating to a public shelter or a second home. The findings of this study can help emergency management and transportation agencies design effective traffic control plans to safely evacuate populations during a hurricane.

This study aims to build an accurate merging position model by incorporating heterogeneity into the accelerated hazard model based on random parameter and latent class models. Three kinds of distribution (Weibull, log-normal, and log-logistic) are compared. The Weibull distribution is found to outperform the others. The latent class accelerated hazard model better captures the unobserved heterogeneity among drivers. The data used in this study can be naturally segmented into two classes. The large class consists of about 80% of drivers and the estimation results are similar to fixed parameter and random parameter models. The smaller one contains drivers that only consider the subject vehicle's speed; these drivers merge rather late. Some of these drivers even merge into the adjacent main lane using the shoulder lane when they cannot find suitable gaps at the end of the auxiliary lane. Considering its good performance, the proposed latent class accelerated hazard model could also bring new insights into road design, such as the determination of the length of the acceleration lane or auxiliary lane. [ABSTRACT FROM AUTHOR]

The aim of the paper is to evaluate the development and main characteristics of Czech milk production productivity and to compare Czech development with the situation in the European Union. From a methodological point of view, a parametric approach in the form of stochastic frontier analysis was applied, the input distance function was estimated, and total factor productivity was examined. The analysis used an unbalanced panel data set, which describes TF14-45 specialist milk production from 27 member states of the European Union in the period 2004-2016 collected in the FADN database. The results showed that in the Czech Republic, the average value of technical efficiency was 94.01% during the analysed time period. Compared to EU member states, this figure was above the EU-13 average (93.71%). Czech milk production in the analysed period and the milk production of almost all other EU countries was characterized by increasing returns to scale. Examination of total factor productivity (TFP) showed that the scale effect and technical efficiency change effect can be considered the main components of TFP changes in Czech milk production. However, the scale effect was more significant in EU-15 countries than the Czech milk sector. [ABSTRACT FROM AUTHOR]

• This study considers time of the day in self-report seat belt use. • This study considers heterogeneity in the analysis. • local coefficients displayed relatively constant variation across space. • GWR model outperformed random parameter and and binary logit model. Time of day and heterogeneity are two common factors that received less attention in self-reported studies concerning seat belt use. Although nighttime seat belt use rate is relatively lower than daytime, previous studies have often not separated the analysis of seat belt use between daytime and nighttime. To incorporate heterogeneity in our analysis, we used random parameters and geographically weighted regression models to explore the factors influencing seat belt use. This study consists of a self-reported seat belt use survey conducted in a sample of 814 respondents aged 18–50 years in six counties (50 zip codes area) in East Tennessee. Comparison of the models indicated that the geographically weighted regression models outperformed other models. Considering the non-stationary test, we learned that the local coefficients displayed relatively constant variation across space in the study area, which indicates behaviors, at least across a large metropolitan area, does not vary spatially. For the random parameter models, age and income had random parameter effects. Perception of receiving a ticket for not wearing a seat belt, uncomfortable seat belt design, driving for a short distance, and driving exposure also had significant negative associations with self-reported seat belt use in both models. Moreover, exposure to educational programs had a significant correlation with seat belt use only in nighttime, whereas this correlation was insignificant for daytime. Findings provide new insight for design and convoy new messages to promote seat belt use by targeting factors predicting seat belt use. Results are discussed in line with road safety analysis. [ABSTRACT FROM AUTHOR]

• We used random-parameter models to investigate drivers' performance. • The influence of DBQ scores and three countermeasures were included in the models. • Ordinary Violation scores explained a considerable part of the drivers' performance. • Some drivers' responses to the countermeasures varied depending on their DBQ scores. The human-environment-vehicle triad and how it relates to crashes has long been a topic of discussion, in which the human factor is consistently seen as the leading cause. Recently, more sophisticated approaches to Road Safety have advocated for a road-driver interaction view, in which human characteristics influence road perception and road environment affects driver behavior. This study focuses on road-driver interaction by using a driving simulator. The objective is to investigate how the driver profile influences driving performance and the effects of three countermeasures (peripheral transverse lines before and after the beginning of the curves and roadside poles in the curves). Fifty-six middle-aged male participants drove a non-challenging rural highway simulated scenario based on a real road where many single-vehicle crashes occurred. The drivers' profiles were assessed through their behavioral history measured by a validated version of the Driver Behavior Questionnaire (DBQ) comprising three dimensions: Errors (E), Ordinary Violations (OV), and Aggressive Violations (AV). The relationship between speed and trajectory measures and drivers' profiles was investigated using random-parameter models with heterogeneity in the means. The models' results showed that the DBQ subscale scores in OV explained a considerable part of the heterogeneity found in drivers' performance. Furthermore, the heterogeneity in the means caused by the DBQ subscale scores in OV and E in the presence of peripheral transverse lines indicates a difference in how drivers react to the countermeasures. The peripheral lines were more efficient than roadside poles to moderate speed but did not positively influence all drivers' trajectories. Although the peripheral lines could be seen as an alternative to change driver behavior in a non-challenging or monotonous road environment, the design used in this study should be reviewed. [ABSTRACT FROM AUTHOR]

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010-2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk-bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk-bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk-bike infrastructure which will assist in investment decision making. [ABSTRACT FROM AUTHOR]

According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio in order to explore the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. The model results indicate that older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), struck by truck, dark-unlighted roadways, six-lane roadways, and speed limits of 40 mph and 50 mph were all factors associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 a.m. to 3:59 p.m.), weekdays, and daylight condition were all factors associated with less severe injuries. This study provides specific safety recommendations so that effective countermeasures can be developed and implemented by policy makers, which in turn will improve overall highway safety. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Indonesian Mathematical Society is the property of Indonesian Mathematical Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The production of services in the accommodation sharing industry is heterogeneous in the sense that listings with different strategic management could adopt distinct technologies. This paper analyses the time-varying efficiency of the peer-to-peer accommodation sector using the input distance stochastic frontier model with random coefficients to accommodate both multi-input and multi-output technology and the technological heterogeneity among listings. An empirical analysis is conducted based on data from Airbnb and HomeAway listings in the Canary Islands (Spain), before, during and after the COVID-19 lockdown (source: AirDNA), in the period January 2019-September 2020 (monthly data). The results show technological heterogeneity between listings and time-varying inefficiency which negatively depends on productivity. Moreover, multi-unit hosts are clearly more efficient than single-unit hosts. A mean efficiency of around 78% during the study period was estimated. • The technological heterogeneity of the peer-to-peer accommodation is analysed. • The effect of the COVID-19 lockdown on the efficiency of listings is calculated. • A mean efficiency of 78% during the study period was estimated. • Multi-unit hosts are more efficient than single unit hosts. [ABSTRACT FROM AUTHOR]

According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio in order to explore the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. The model results indicate that older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), struck by truck, dark-unlighted roadways, six-lane roadways, and speed limits of 40 mph and 50 mph were all factors associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 a.m. to 3:59 p.m.), weekdays, and daylight condition were all factors associated with less severe injuries. This study provides specific safety recommendations so that effective countermeasures can be developed and implemented by policy makers, which in turn will improve overall highway safety.

For the purposes of long-term planning and budgeting, infrastructure user cost allocation, and financial need forecasts, infrastructure agencies seek knowledge of the annual expenditure levels for maintaining their assets. Often, this information is expressed in dollars per unit dimension of the infrastructure and is estimated using observed data from historical records. This paper presents an artificial neural network (ANN) approach for purposes of estimating annual expenditures on infrastructure maintenance and demonstrates the application of the approach using a case study involving rural interstate highway pavements. The results of this exploratory study demonstrate that not only is it feasible to use ANN to derive reliable predictions of annual maintenance expenditures (AMEX) at aggregate level, but also it is possible to identify the influential factors of such expenditures and to quantify the sensitivity of AMEX to such factors. [ABSTRACT FROM AUTHOR]

This paper explains a modeling approach that offers better understanding of the routing strategies taken by evacuees to reach a safe destination during hurricane evacuation. Route choice during evacuation is a complex process because evacuees may prefer to take the usual or familiar route on the way to the destination, or they might follow the routes recommended by the emergency officials. Depending on the condition of the traffic stream, sometimes they might switch to a different route to obtain better travel time from the one initially attempted, i.e., the routing behavior is random. By using data from Hurricane Ivan, a mixed (random parameters) logit model is estimated which captures the decision making process on what type of route to select while accounting for the existence of unobserved heterogeneity across households. Estimation findings indicate that the choices of evacuation routing strategy involve a complex interaction of variables related to household location, evacuation characteristics, and socioeconomic characteristics. The findings of this study are useful to determine the manner in which different factions of people select a type of route for a given sociodemographic profile during an evacuation. [ABSTRACT FROM AUTHOR]

This article examines farmers' preferences for both cropping and management practices, which are of policy interest because of their environmental impact. We present the results of a choice experiment survey of all agricultural decision makers in the Camargue region. A latent class model identified three classes. The main class encompasses farmers complying with the norms of the Protected Geographical Indication (PGI): Riz de Camargue. We estimated the monetary value of each of the relevant agricultural practices. Our results make a strong case for differentiating incentives to encourage environmentally friendly practices and identify the diversity of values attached to the main components of rice cropping technology in the area. Estimates of the implicit prices indicate that most rice growers can be persuaded to adopt environmentally friendly practices. These findings could help in designing targeted contracts according to farmers' preferences, in line with conservation or environmental objectives. [ABSTRACT FROM AUTHOR]

Highlights: [•] A behavioral model to estimate decision to evacuate and actual evacuation. [•] An ordered probit model is developed to understand this behavior. [•] Model estimation using hurricane Ivan data. [•] Important insights and policy recommendations are discussed. [Copyright &y& Elsevier]

Abstract: Pedestrian–vehicle crashes remain a major concern in New York City due to high percentage of fatalities. This study develops random parameter logit models for explaining pedestrian injury severity levels of New York City accounting for unobserved heterogeneity in the population and across the boroughs. A log-likelihood ratio test for joint model suitability suggests that separate models for each of the boroughs should be estimated. Among many variables, road characteristics (e.g., number of lanes, grade, light condition, road surface, etc.), traffic attributes (e.g., presence of signal control, type of vehicle, etc.), and land use (e.g., parking facilities, commercial and industrial land use, etc.) are found to be statistically significant in the estimated model. The study also suggests that the set of counter measures should be different for different boroughs in the New York City and the priority ranks of countermeasures should be different as well. [Copyright &y& Elsevier]

The existing research on motorcycle safety has shown that single-vehicle motorcycle crashes (SVMC) account for a higher fatality rate than other types of crashes. Also, motorcycle safety has become one of the critical traffic safety issues in many developing countries, such as Pakistan, due to the growing number of motorcycles and lack of sufficient relevant infrastructure. However, the available literature on SVMC and motorcycle safety in developing countries is limited. Therefore, the present study attempted to investigate the factors that contribute to the injury severity of SVMC in a developing country, Pakistan. For this purpose, a random parameter logit model with heterogeneity in means and variances is developed using two years of data extracted from the road traffic injury research project in Karachi city, Pakistan. The study's findings show that the presence of pillion passengers and young motorcyclists indicators result in random parameters with heterogeneity in their means and variances. The study's results also reveal that the summer, morning time, weekends, older motorcyclists, collisions with fixed objects, speeding, and overtaking are positively, while younger motorcyclists and the presence of pillion passengers are negatively associated with fatal crashes. More importantly, in the particular Pakistan's context, female pillion passenger clothes trapped in the wheel, riding under the influence, intersections, U-turns, and collisions due to loss of control are also found to significantly influence the injury severity of SVMC. Based on these research findings, multiple appropriate countermeasures are recommended to enhance motorcycle safety in Pakistan and other developing countries with similar problems.

Abstract: Many economic decisions are made jointly within households. Running an experiment on intertemporal choice, we investigate the relative influence of spouses on joint household decisions. We let each spouse first decide individually and then jointly with the other spouse. We propose the use of a random parameter probit model to measure the relative influence of spouses on joint decisions. We find that joint choices are in between husbands’ and wives’ choices with respect to impatience, indicating that both spouses have an influence on joint decisions. However, we estimate that in 99% of households husbands have a stronger influence than wives. [Copyright &y& Elsevier]

Unobserved preference heterogeneity has been widely recognized as a critical issue not only for modelling choice behaviour, but also for policy analysis. This paper examines alternative approaches for incorporating heterogeneity in recreational demand. We apply a hybrid model combining discrete and continuous heterogeneity representations of tastes to capture the defining features of both the latent class and the random parameter logit specifications. This model allows for the joint estimation of discrete segments and within segment heterogeneity providing a richer interpretation of preference heterogeneity. A database of recreational trips to forest sites in Mallorca has been used to compare the empirical performance of this hybrid approach with common specifications such as the conditional logit, the random parameter logit, and the latent class model. [ABSTRACT FROM AUTHOR]

Benefit transfer is a cost-effective method for estimating the value of environmental goods that relies on information obtained in previous studies. The multiattribute approach of choice experiments should provide advantages in terms of benefit transfer, allowing differences in environmental improvements between sites as well as differences in socioeconomic and attitude characteristics between respondent populations. This article investigates the capability of choice experiment method to be used in environmental benefit transfer when a random parameters approach is used to allow for preference heterogeneity: we find that the inclusion of respondents' taste heterogeneity reduces the magnitude of the transfer error. [ABSTRACT FROM AUTHOR]

The interest for wetlands is increasing, not only because of the possibility of a cost-efficient uptake of nutrients, but also because wetlands can be designed to provide other services. What values that are supplied depend largely on the design. There are numerous different design options, and different actors may promote different alternatives. Whether we want to design a wetland for nutrient retention alone, or one that also serves other interests, policy makers need information about the value of different options. Conducting a choice experiment, we are able to identify attributes that increase and decrease citizens perceived value of wetlands. Using a random parameter model we find that biodiversity and walking facilities are the two greatest contributors to welfare, while a fenced waterline and introduction of crayfish decrease welfare. [Copyright &y& Elsevier]

The aim of the study was to define and analyse a prediction model for the relationship between tree height and diameter of Pinus kesiya (Royle ex Gordon) grown in the forest plantations of Zambia and Zimbabwe. The modelling was based on an equation of the power type. The development of the height–diameter pattern was assumed to be dependent on both tree size and stand characteristics. The tree diameter at breast height, stand age, dominant height and diameter were used as predictors of the tree height. Due to the spatially hierarchical (stands, trees) and temporal (measurement occasions) correlation structures in the data, the basic assumption about noncorrelated error terms did not hold. Therefore, the generalised least squares method was used in the parameter estimation of the random parameter model. In model applications, the development of the height–diameter pattern is predicted with the parameters of the fixed model part. The variance estimates for the random stand, measurement and tree effects can be used in model calibration by applying the standard linear prediction theory. These results lead to the recommendation that the power type of model be used as the predictor of the height–diameter relationship of P. kesiya stands in the tree plantations of southeastern Africa. [Copyright &y& Elsevier]

• Investigated the willingness to share private parking spaces. • Examined the choice between flexible and fixed modes of sharing parking places. • Explored the decision making under uncertainty. • Developed a hybrid random-parameter logit-cumulative prospect theoretic model. Shared parking allows the effective use of undersupplied parking spaces and contributes to the alleviation of urban parking problems, traffic congestion, environmental pollution, and other negative externalities of traffic. However, little is known about the acceptance of shared parking by consumers of a different socio-demographic profile. To understand the feasibility and potential success of shared parking, this paper develops a stated choice experiment with three choice options: fixed mode shared parking, flexible mode shared parking and not interested, to investigate parking space owners' propensity to engage in shared parking under varying conditions. Because the demand for shared parking is uncertain, the revenues owners may generate are uncertain. As one of the most popular theories of decision making under uncertainty, the cumulative prospect theory is incorporated into a multinomial logit model to capture the decision problem in which some variables are uncertain and others are not. The revenue that owners expect shared parking can bring is used as the reference point to differentiate between gains and losses. Gains refer to outcomes that exceed the reference point, while losses refer to outcomes that fall short. To examine unobserved heterogeneity, a random parameter version of the model is specified to estimate the distribution of decision weights across the sample. Results show that socio-demographic characteristics, context variables, revenues and psychological concerns are all important factors in explaining parking space owners' propensity to engage in platform-based shared parking schemes. Incorporating unobserved heterogeneous improves the overall goodness-of-fit of the model. Understanding parking space owners' propensity to share their parking spaces in relation to their psychological concerns and uncertain conditions is critical to improve shared parking policies. The results of this paper may help designers and planners in the delivery of shared parking services and promote the success and future growth of the shared parking industry. [ABSTRACT FROM AUTHOR]

Understanding pedestrian crash causes and contributing factors in developing countries is critically important as they account for about 55% of all traffic crashes. Not surprisingly, considerable attention in the literature has been paid to road traffic crash prediction models and methodologies in developing countries of late. Despite this interest, there are significant challenges confronting safety managers in developing countries. For example, in spite of the prominence of pedestrian crashes occurring on two-way two-lane rural roads, it has proven difficult to develop pedestrian crash prediction models due to a lack of both traffic and pedestrian exposure data. This general lack of available data has further hampered identification of pedestrian crash causes and subsequent estimation of pedestrian safety performance functions. The challenges are similar across developing nations, where little is known about the relationship between pedestrian crashes, traffic flow, and road environment variables on rural two-way roads, and where unique predictor variables may be needed to capture the unique crash risk circumstances. This paper describes pedestrian crash safety performance functions for two-way two-lane rural roads in Ethiopia as a function of traffic flow, pedestrian flows, and road geometry characteristics. In particular, random parameter negative binomial model was used to investigate pedestrian crashes. The models and their interpretations make important contributions to road crash analysis and prevention in developing countries. They also assist in the identification of the contributing factors to pedestrian crashes, with the intent to identify potential design and operational improvements., (Copyright © 2015. Published by Elsevier Ltd.)