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Architectural designers are adopting changes in the sector of traffic management. Advancements and innovations mean a lot in designing predictive models. This is practical in urban areas with high traffic flow. Applying the scenario of smart highways, we develop a model based on deep learning techniques to address challenges faced in this sector. We further our study by utilizing Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to capture temporal dependencies and spatial correlations within traffic data. A comprehensive dataset, including real-time sensor data from intelligent highways, was used to train and validate the model’s performance. The results of this study show that the deep learning model significantly outperforms traditional methods. This provides superior accuracy and faster response times when predicting traffic conditions. Also, the model demonstrates robustness in adapting to fluctuating traffic patterns, like congestion, accidents, and peak-hour traffic. This makes the model to be highly reliable for dynamic traffic management. By enabling efficient traffic flow predictions, the model holds promise for reducing congestion, enhancing road safety, and optimizing the overall performance of intelligent highway systems. Thus, this paper provides a strong foundation for the implementation of intelligent transportation systems by highlighting the transformative potential of deep learning in future traffic management strategies.

Rapid motorization and uncertainty in urban growth patterns make parking space management a serious task, especially in middle-income developing countries, and this has severe social, economic, and environmental repercussions including increased congestion, crash frequency, fuel and time consumption, and air pollution. Due to the complexity of the urban transportation issue and the wide variety of variables involved, a multicriteria assessment is essential. This study used fuzzy logic and geographical information systems (GIS) to develop a multi-criteria decision making (MCDM) model for managing parking in Shiraz’s central business district (CBD). The literature was mined for information on the variables that affect parking site placement, and a poll of experts ($n=11$) was used to determine their relative importance. The distance to travel attraction centers, distance to roads, land price, population density, and available land for multi-storey parking were among the factors considered. Meanwhile, the parking space shortage for each TAZ is calculated by subtracting the estimated parking space supply from the estimated parking space demand. An overlay of these two layers distinguishes locations that are in parking shortage zones and also meet multiple criteria. The results may aid policymakers in controlling parking demand by pinpointing the most promising places for investment.

We study an original scheme based on distributed feedforward neural networks, aimed at modelling several queueing systems in cascade fed with bursty traffic. For each queueing system, a neural network is trained to anticipate the average number of waiting packets, the packet loss rate and the coefficient of variation of the packet inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the packet inter-arrival time. The latter serves for the calculation of the coefficient of variation of the cell inter-arrival time of the aggregated traffic which is fed as input to the next neural network along the path. The potential of this method is successfully illustrated on several single server FIFO (First In, First Out) queues and on small queueing networks made up from a combination of queues in tandem and in parallel fed by a superposition of ideal sources. Our long-term goal is the design of preventive control strategy in a multiservice communication network.

In this paper, we present an analysis of a restricted bi-level optimization model proposed by [19] applied to telecommunication network pricing. This framework provides a generalization of telecommunication resource allocation/shadow price-based schemes such as those of [9] and [23], and permits the optimization of link prices with an aim towards revenue maximization. The analysis of the model is based on numerical experiments that we conducted for a variety of networks with varying parameters. We show that in practice the goal of maximizing operator revenue is not always attainable. We present some such cases, as well as valid alternative objectives for these instances.