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Researchers have studied processes of improving road traffic-safety culture by explicitly evaluating the socio-psychological phenomenon of traffic-risk. The implicit traffic-system cues play an important role in explaining urban traffic-culture. This paper aims to ascertain an interpretive framework of the alternative processes of road traffic safety culture is antecedent to promote traffic-safety behaviour in Indian urban context. Subsequently, the authors discussed the reasons for those relationships exists.

Four experts of the urban traffic-safety domain participated in total interpretive structural modelling (TISM) study by completing an interpretive consensus-driven questionnaire. The drafted interpretive model was evaluated for road users proactive action orientation about the traffic-safety decision.

The evolved directed graph (digraph) of the culture of urban traffic-safety management was a serial three-mediator model. The model argued: In the presence of traffic-risk cues, people may become apprised to safety goals that initiate traffic-safety action. Consequently, expectancy-value evaluation motivates the continuation of traffic-safety intention that may lead to the implementation of adaptation plan (volitional control), thus habituating road users to traffic-safety management choice.

The modellers of traffic psychology may empirically estimate and test for the quality criteria to ascertain the applicability of the proposed mechanism of urban traffic-safety culture. The decision-makers should note the importance of arousal of emotions regarding traffic-risk, reduce the impact of maladaptive motivations and recursively improve control over safety actions for promoting safety interventions.

The authors attempted to induce an interpretive model of urban traffic-safety culture that might augment extant discussion regarding how and why people behave in an urban traffic system.

In this paper, an intelligent information assisted communication transportation framework (II-CTF) has been introduced to reduce congestion, data reliability in transportation and the environmental effects.

The main concern of II-CTF is to mitigate public congestion using current transport services, which helps to improve data reliability under hazardous circumstances and to avoid accidents when the driver cannot respond reasonably. The program uses machine learning assistance to predict optimal routes based on movement patterns and categorization of vehicles, which helps to minimize congestion of traffic.

In II-CTF, scheduling traffic optimization helps to reduce the energy and many challenges faced by traffic managers in terms of optimization of the route, average waiting time and congestion of traffic, travel, and environmental impact due to heavy traffic collision.

The II-CTF definition is supposed to attempt to overcome some of the problems of the transportation environment that pose difficulties and make the carriage simpler, safer, more efficient and green for all.

The purpose of this paper is to develop pansystems‐styled traffics, logistics and knowledge rediscovery. The core logoi are the generalized resources//F* and their circulation//C*. The research presents a new outlook to mathematics, physics, traffic, communication, logistics, KD, internet, computer, translation, simulation, information, life‐systems, logic or reasoning, memory or storage, process, transaction, finance, education, inherent, topology, evolution, etc. and then certain mechanism among them and related mutuality can be unveiled.

A flexible combination of philosophy, mathematics and technology is embodied. The paper is an application of pansystems methodology to approach the following topics: pansystems resource, circulation: traffics, logistics and KD; pansystems variational principle and pan‐circulation; pansystems logistics; pansystems network, supply chain and bullwhip effect; pansystems innovation and knowledge rediscovery.

All of the topics concerned are reduced to the actualization of pansystems variational OR and classification‐repetition‐PanOR, which possess a transfield nature.

This paper provides the framework and concretion principles of pansystems research on traffics, logistics, supply chain, bullwhip effect, innovation and related applications, and presents a new world outlook, which leads to some new comprehension to KD and WHF* <world*history*future*>, SEM* <society* economy*management*>.

This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.

The purpose of this paper is to study the traffic‐police enforcement process and develop models to improve enforcement effectiveness given substantial budgetary and resource constraints.

The formulation crosses the concepts of lean manufacturing and linear programming. Traffic police officers, automated machines and the back‐office are modeled in a similar manner to that of a manufacturing plant, working together to achieve ticket production as a function of quantity and quality, based on a preferential ranking of offence types.

Using data from the Israeli traffic police over a six‐year period, the case study shows that given available resources, it is possible to retain ticket quantity whilst significantly improving ticket quality as defined in the road safety literature. The case study shows a 24 per cent increase in quality ticket processing whilst taking into account the court summons constraint and maintaining throughput levels. This draws from changes in the method of ticket‐production, production of warnings rather than tickets in certain cases and the application of new technologies.

The results are limited by the current lack of data and require a cost‐benefit analysis in order to further develop certain parameters.

The application of the approach improves the holistic planning of traffic enforcement activities as well as providing specific details, such as the number and distribution of ticket production.

This research merges three disciplines; operations research, road safety and operations management, generating a methodology for the planning and control of traffic police ticket issuance, which has not been analyzed in the literature to date.