The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes a widely acknowledged trend in the development of urban infrastructure. The proliferation of various types of transportation vehicles exacerbates the complexity of traffic regulation. Intelligent transportation systems, leveraging real-time traffic status prediction technologies, such as velocity estimation, emerge as viable solutions for the efficacious management and control of urban road networks. The objective of this project is to address the complex task of increasing accuracy in predicting traffic conditions on a big scale using deep learning techniques. To accomplish the objective of the study, the historical traffic data of Paris and Istanbul within a certain timeframe were used, considering the impact of variables such as speed, traffic volume, and direction. Specifically, traffic movie clips based on 2 years of real-world data for the two cities were utilized. The movies were generated with HERE data derived from over 100 billion GPS (Global Positioning System) probe points collected from a substantial fleet of automobiles. The model presented by us, unlike the majority of previous ones, takes into account the cumulative impact of speed, flow, and direction. The developed model showed better results compared to the well-known models, in particular, in comparison with the SR-ResNet model. The pixel-wise MAE (mean absolute error) values for Paris and Istanbul were 4.299 and 3.884 respectively, compared to 4.551 and 3.993 for SR-ResNET. Thus, the created model demonstrated the possibilities for further enhancing the accuracy and efficacy of intelligent transportation systems, particularly in large urban centres, thereby facilitating heightened safety, energy efficiency, and convenience for road users. The obtained results will be useful for local policymakers responsible for infrastructure development planning, as well as for specialists and researchers in the field. Future research should investigate how to incorporate more sources of information, in particular previous information from physical traffic flow models, information about weather conditions, etc. into the deep learning framework, as well as further increasing of the throughput capacity and reducing processing time.

This paper is an attempt to advance research on walking at a neighborhood level of analysis for people with disabilities by proposing a theoretical model that combines the knowledge of two disciplines: traffic planning and environmental psychology. The aim is to provide guidance for a discussion and a plan for future interdisciplinary investigations by proposing a model that accounts for the dynamic interaction between environmental characteristics, human processes, and walking experience among individuals with a disability. For this purpose, traffic planners, and environmental psychologists came together to discuss theories, concepts, and thematic relevance in a series of focus group meetings. These meetings led to the selection of the Human Environment Interaction (HEI) model, originally developed from the field of environmental psychology and operationalized to describe how walking experiences result from the interplay between individual abilities, emotional processes, and the physical and social characteristics of the environment (Küller, 1991). The proposed model aims to sustain interdisciplinary discussion and research planning around the topic of neighborhood walking for people with disabilities. By operationalizing each dimension in the model, a good fit between groups with disabilities and individual differences associated with walking experiences is assumed, which, in turn, will have the potential to provide a more conscious analysis of wellbeing-related outcomes, such as usability of the environment, frequency of mobility, and quality of life. However, to improve understanding of urban walking at a neighborhood level for people with disabilities, empirical studies must be carried out to test the proposed model.