Even though the traffic systems in developed countries have been analyzed rigorously and operated efficiently, the same does not generally hold for developing countries due to inadequate planning, design, and operations of their transportation systems. Because of inherent differences between internal infrastructures, the strategies deployed in developed countries may not be amenable to developing ones. Besides, developing countries’ traffic systems are not well-studied in the literature to the best of our knowledge. For example, it is yet to explore how a developed country’s lane-based traffic flow would perform in the context of a developing country, which generally experiences non-lane-based traffic. As such, by using our newly developed traffic simulator ‘RoadBird,’ we investigate outcomes of both lane-based and non-lane-based traffic from the contexts of both developing and developed countries. To do so, we run simulations over real road topologies (extracted from the GIS maps of major cities such as Dhaka, Miami, and Riyadh), considering different scenarios such as lane-based or non-lane-based flows, homogeneous or heterogeneous traffic, with or without pedestrians, etc. We also incorporate various car-following and lane-changing models to mimic traffic behaviors and investigate their performances. While the lane changing dilemma remains an open research question, our experimental evidence indicates: 1) lane-based approaches will not necessarily perform better in the case of currently-adopted non-lane-based scenarios, and 2) non-lane-based strategies may benefit system performance in lane-based scenarios while having heavy mixed traffic. Nonetheless, we reveal several new insights for on-road experiences both in developing and developed countries.

Derailments present a frequent phenomenon in several developing countries, which result in massive loss of property along with death tolls. For preventing derailments, a real-time automated system is needed to detect uprooted or faulty rail blocks. One of the solutions in this context is to sense the vibration of the rail track having an incoming train and transmit the information to the train notifying it about the condition of the rail track ahead. However, existing studies in this regard are yet to present a pragmatic solution that enables much-demanded long-distance networking to transmit the sensed data. The demand for long-distance network communication between the sensor nodes and the incoming train is unavoidable, as stopping the train after sensing an uprooted or faulty rail block ahead needs a considerable response time and distance. Therefore, in this paper, we develop a low-cost, long-range, and highly reliable mobile multi-hop networking scheme to successfully transmit data sensed from rail tracks to an approaching train at a distance of around 2000m. By considering the effect of Fresnel’s Region in our study, we determine the suitable placement of the networking module on the rail track, which leads us to achieve a delivery ratio of more than 99%. We confirm this finding through rigorous experiments over a real testbed scenario enabling mobile multi-hop networking.

Inefficient traffic signal control system is one of the most important causes of traffic congestion in the cities of developing countries such as Bangladesh, India, Kenya, etc. This can be mitigated by adopting a decentralized traffic-responsive signal system, where vehicle detection is performed on the road through different image-based deep learning architectures amenable to limited-resource embedded platforms as available in developing countries. Deep learning architectures currently available in this regard demand high computational resources to achieve higher inference speed and better accuracy. Besides, the few existing limited-resource deep learning architectural alternatives neither attain higher inference speed nor substantial accuracy due to not overcoming the inherent limitations. To this extent, in this study, we propose a novel limited-resource deep learning architecture, namely DhakaNet, for real-time vehicle detection in on-road (street-view) traffic images. Our proposed architecture leverages enhancing Cross-Stage Partial Network and Path Aggregation Network to build the backbone and head networks, respectively. Besides, we develop a novel multi-scale attention module to extract multi-scale meaningful features from the images, where the developed multi-scale attention module boosts the detection accuracy at the cost of small overhead. Rigorous experimental evaluation of our proposed DhakaNet over three benchmark street-view traffic datasets such as DhakaAI, IITM-HeTra-A, and IITM-HeTra-B shows up to 51% faster inference speed at a similar accuracy, or up to 13% higher accuracy at a similar inference speed compared to other state-of-the-art limited-resource deep learning architectural alternatives.