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Since its EU accession, Poland has invested strongly in the development of fast road transport network. As a result, the total length of modern, high-speed roads has increased from around 500 km in 2005 to over 3000 km in 2015. Yet, while the positive impact of transport infrastructure investment on overall accessibility is unquestionable there are no studies that assess its influence on economic development of particular regions. This paper applies a regional dynamic CGE model to measure the effects of big transport infrastructure investments in Polish NUTS2 regions. We use data on both investment spending and accessibility improvement (expressed as a reduction in transport margins) in order to distinguish between possible short and long term impacts. We find that there exist significant disparities in the impact between regions with high share of major road infrastructure investment undertaken by private investors and the ones that relied fully on public funding. In the case of the former, the lack of analyzed investment would lead to relatively significant decrease in real GDP or average employment. In the case of the latter, the impact of major road infrastructure investment is almost negligible.

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation.

Transportation resilience, as a component of city sustainability, plays a crucial role in the daily management and emergency response of urban road systems. With coastal cities becoming increasingly vulnerable to typhoons, rainstorms, and other disasters, it is essential to assess the resilience of urban road transportation in a refined and differentiated approach. Existing resilience assessment methods often overlook significant biases, neglecting the dynamic response of road traffic and non-stationary characteristics of traffic systems. To address these limitations, we develop a quantitative resilience assessment method for urban road transportation during rainfall that is based on the improved Resilience Triangle. The method is applied to DiDi urban traffic speed and meteorological data of Shenzhen, China, from April to September 2018, with a focus on Typhoon Mangkhut as an extreme weather case. By analyzing transportation resilience variations across road densities, road hierarchies, and rainfall scenarios, we found that road densities and rainfall intensities explain resilience variations better than road hierarchies. Specifically, as accumulative precipitation exceeds 100 mm, a substantial surge in loss of performance is observed. Typhoon rainfalls result in a greater loss in urban road traffic compared to general rainfalls. The results offer valuable insights for urban road planning, traffic emergency management, and transportation resilience construction in the face of increasingly severe weather challenges.

Traffic forecasts are employed in the toll road sector, inter alia, by private sector investors to gauge the bankability of candidate investment projects. Although much is written in the literature about the theory and practice of traffic forecasting, surprisingly little attention has been paid to the predictive accuracy of traffic forecasting models. This paper addresses that shortcoming by reporting the results from the largest study of toll road forecasting performance ever conducted. The author had access to commercial-in-confidence documentation released to project financiers and, over a 4-year period, compiled a database of predicted and actual traffic usage for over 100 international, privately financed toll road projects. The findings suggest that toll road traffic forecasts are characterised by large errors and considerable optimism bias. As a result, financial engineers need to ensure that transaction structuring remains flexible and retains liquidity such that material departures from traffic expectations can be accommodated.

Robust road transportation networks are essential to facilitate truck movements between regions. Disruptions along certain road links could degrade the network’s robustness and curtail regional commerce. A network is considered robust if the network-wide travel cost does not change significantly before and after disruptions. The network robustness index (NRI) is a well-known measure for examining the effect of reduced capacity on one or more links. It has been mainly applied to urban networks and passenger trips. In this paper, the NRI is applied to assess the robustness of the Ontario-wide truck network. Truck flows between the 49 census divisions within Ontario and 25 external zones for the year 2012 are modeled on the network of 35,254 road links and 14,444 associated nodes. Several scenarios of incrementally reduced capacity on specific critical links are devised and simulated. The NRI is calculated for each scenario to identify the level of network robustness. The locational effects of the capacity reductions were explored with the help of maps to assess the change in traffic volumes of the simulated scenarios relative to the base case. As expected, traffic diffuses or shifts to neighboring links when capacities are reduced. These results can be useful in infrastructure planning and the design of alternative routes to minimize the negative impact on traffic flows when disruptions occur.

Modern rail/road transportation systems are critical to global travel and commercial transportation. The improvement of transport systems that are needed for efficient cargo movements possesses further challenges. For instance, diesel-powered trucks and goods trains are widely used in long-haul unimodal transportation of heavy cargo in most landlocked and developing countries, a situation that leads to concerns of greenhouse gases (GHGs) such as carbon dioxide coming from diesel fuel combustion. In this context, it is critical to understand aspects such as the use of some parameters, variables and constraints in the formulation of mathematical models, optimization techniques and algorithms that directly contribute to sustainable transportation solutions. In seeking sustainable solutions to the bulk cargo long-haul transportation problems in Zambia, we conduct a systematic review of various transportation modes and related mathematical models, and optimization approaches. In this paper, we provide an updated survey of various transport models for bulk cargo and their associated optimized combinations. We identify key research challenges and notable issues to be considered for further studies in transport system optimization, especially when dealing with long-haul unimodal or single-mode heavy cargo movement in countries that are yet to implement intermodal and multimodal systems.

Animals may be subjected to various stressors during transport, which may compromise their health and welfare as well as meat quality. In the chain of operations between a farm and a slaughterhouse, animal transport is probably the most stressful and injurious stage. Data on mortality is commonly collected at slaughterhouse as a retrospective indicator of animal welfare during transport. Ten-year prevalence of mortality of all the species and categories of animals (cattle, pigs, goats, sheep, poultry, rabbits and ostriches) regularly scheduled for slaughter in the Czech slaughterhouses was assessed as dead on arrival after road transport from 2010 to 2019. Among livestock, the highest mortality was found in pigs (0.065%); statistically higher compared to cattle (0.027%) and sheep (0.015%). In animals shipped in containers (rabbits, broiler chickens, end-of-lay hens, turkeys, geese and ducks), the highest prevalence was found in laying hens (0.507%), statistically higher compared to broiler chickens (0.425%) and rabbits (0.199%). The lowest prevalence was observed in geese (0.003%). There was a trend for decreasing death losses of pigs in more recent years and losses in broiler chickens and ducks increased. The results indicate that the current transport conditions should be re-evaluated for poultry. Emphasis should be put on the assessment of animal fitness before transport. This is especially important for animals at the end of their production cycle such as dairy cows, sows, and laying hens. They were more likely to die during the journey. Highlights Dead on arrival is an animal-based measure commonly applied at slaughterhouse as a retrospective indicator of animal welfare during transport. In a cross-species comparison, the highest prevalence was observed for animals transported in cages and for end of production cycle animals. To reduce mortality, several factors have to be considered by the meat industry other than the species-related ability to cope with transport.

We develop a short turning model using demand information from station to station within a single bus line-single period setting, aimed at increasing the service frequency on the more loaded sections to deal with spatial concentration of demand considering both operators’ and users’ costs. We find analytical expressions for optimal values of the design variables, namely frequencies (inside and outside the short cycle), capacity of vehicles and the position of the short turn limit stations. These expressions are used to analyze the influence of different parameters in the final solution. The design variables and the corresponding cost components for operators and users (waiting and in-vehicle times) are compared against an optimized normal operation scheme (single frequency). Applications on actual transit corridors exhibiting different demand profiles are conducted, calculating the optimal values for the design variables and the resulting benefits for each case. Results show the typical demand configurations that are better served using a short turn strategy.

Water and feed are needed for livestock during their long-duration road transportation. However, limited information is available on the need to supply water and feed to livestock at temporary holding stations after road transportation. This study aimed to evaluate the effect of providing water and feed at holding stations on the welfare of goats in mimic surroundings. A total of 24 Guizhou black goats were randomly divided into three groups of eight goats each as follows: deprived of water and feed (TRT0), supplemented with water ad libitum (TRT1), and supplemented with water and feed ad libitum (TRT2). Blood and rumen fluid samples were collected before loading (denoted as “PRE” in this article) and after transport (denoted as “POST” in this article). Statistical analysis was performed via the SAS procedure PROC MIXED. The 10-h road transportation period reduced body weight in TRT0 goats ( p < 0.05) but not in TRT1 and TRT2 ( p > 0.05). TRT0 and TRT1 goats had POST plasma glucose concentrations above their PRE values ( p < 0.05). The PRE-plasma urea nitrogen (PUN) levels were higher in TRT2 compared to TRT0 ( p < 0.05) goats, while the POST–PUN levels increased in TRT1 compared to TRT0 goats. The POST non-esterified fatty acid (NEFA) concentration was higher for TRT0 compared to that in TRT1 and TRT2 ( p < 0.05) goats. No difference was observed for plasma profiles of malondialdehyde (MDA) and superoxide dismutase (SOD) ( p > 0.05). TRT2 goats had higher POST glutathione peroxidase (GSH-Px) activity than TRT0 and TRT1 ( p < 0.05) goats. TRT0 goats had higher POST plasma glucagon (GC) compared to TRT2 ( p < 0.05) and had increased values compared to their own PRE level as a result of road transportation ( p < 0.05). TRT2 goats resulted in a lower POST plasma heat-stressed protein-70 (HSP-70) level than TRT0. There was no difference in ruminal pH ( p > 0.05). Ruminal total VFA (acetate, propionate, butyrate), and the NH 3 -ammonia profiles showed a decrease ( p < 0.05) after transportation in all groups. Ruminal microcrystalline cellulose, xylanase, cellobiase, and carboxymethyl cellulose activities were unaffected ( p > 0.05). These combined results imply that water and feed supplementation to livestock can effectively alleviate stress responses in goats subjected to road transportation and emphasize the necessity to establish water and feed supplies even at a temporary holding pen.

Studies applying Multi-Criteria Decision Analysis (MCDA) to evaluate Road Transportation Fuels and Vehicles (RTFV) rely on a wide variety of evaluation criteria and appear to lack a structured and consistent way of criteria selection. This leads to non-transparent and not easily comparable evaluation results. To address this issue, a methodological framework is developed to systematically identify and select relevant MCDA-evaluation criteria for the assessment of RTFV. The methodological framework is based on Life Cycle Sustainability Analysis (LCSA) and considers environmental, economic, and social criteria that are complemented with a technical pillar. The scope of the analysis is further enlarged by considering positive and negative externalities. The first part of the framework follows the LCSA approach and requires the analyst to clearly define the context of the analysis. The second part is to decompose the problem by developing criteria categories along the relevant life cycle for each of the evaluation dimensions. This decomposition process helps decision makers to easily identify and select relevant criteria with clear added value within the context of the analysis. In an exemplary application, the developed methodological framework is used to identify relevant criteria for the evaluation of RTFV alternatives for an island aiming at energy self-sufficiency.