Efficiency Of Transportation Network Research Articles

First-passage properties of bundled networks.

Bundled networks, obtained by attaching a copy of a fiber structure to each node on the base structure, serve as important realistic models for the geometry and dynamics of nontranslationally invariant systems in condensed matter physics. Here, we analyze the first-passage properties, including the mean first-passage time, the mean-trapping time, the global-mean first-passage time (GFPT), and the stationary distribution, of a biased random walk within such networks, in which a random walker moves to a neighbor on base with probability γ and to a neighbor on fiber with probability 1-γ when the walker at a node on base. We reveal the primary properties of both the base and fiber structure, which govern the first-passage characteristics of the bundled network. Explicit expressions between these quantities in the bundled networks and the related quantities in the component structures are presented. GFPT serves as a crucial indicator for evaluating network transport efficiency. Unexpectedly, bases and fibers with similar scaling of GFPT can construct bundled networks exhibiting different scaling behaviors of GFPT. Therefore, bundled networks can be tailored to accommodate specific dynamic property requirements by choosing a suitable base and fiber structure. These findings contribute to advancing the design and optimization of network structures.

Advancing Roadway Safety through Intelligent Directional Indicators

This Roadway safety remains a critical concern worldwide, with millions of accidents occurring annually, leading to significant loss of life and economic damage. Conventional directional indicator systems, reliant on manual control or mechanical switches, often fall short in providing timely and accurate directional information to other road users, resulting in confusion and potential accidents. To address this challenge, the project "Advancing Roadway Safety through Intelligent Directional Vehicle Indicators" proposes a novel solution leveraging advanced technologies such as the ESP32 microcontroller and the ADXL345 accelerometer. This project aims to develop a robust system capable of accurately detecting and signaling left and right turns in real-time, thereby enhancing situational awareness and reducing the risk of accidents caused by signaling errors or ambiguity. The system integrates hardware components including the ESP32 controller, ADXL345 accelerometer, and OLED display, along with software components comprising firmware for the ESP32, algorithms for directional detection using accelerometer data, and display logic for the OLED display. Through a comprehensive methodology involving calibration of the accelerometer, data processing, and integration of hardware and software components, the system achieves enhanced safety, reliability, integration, and efficiency. Experimental results demonstrate the effectiveness of the proposed system in providing accurate and timely directional indicators, with minimal false positives or negatives under various environmental conditions. The project contributes to advancing roadway safety and lays the foundation for the widespread adoption of intelligent transportation systems, offering significant potential for reducing accidents and improving the efficiency of transportation networks

Construction and Characteristics Analysis of the Xi’an Public Transport Network Considering Single-Mode and Multi-Mode Transferring

The connectivity of the urban public transport network and the convenience of transfers between modes of transit are important factors that affect whether passengers choose public transport. Identifying the key nodes that affect network connectivity, direct access, transfers, and clarifying the main factors that restrict the network efficiency play important roles in improving the efficiency of the public transport network and establishing a “green city”. On this premise, this paper constructs two single-layer networks and a composite network that can reflect the transfer relationship between ‘bus–bus’, ‘metro–metro’, and ‘metro–bus’ based on the method of Space-P. The composite network realizes the integration study of homogeneous and heterogeneous stops, lines, and transfer relationships in the public transport network. At the same time, five kinds of centrality indexes are applied to the transport transfer network, and the significance of these indexes in the network is explained. Through the comprehensive analysis of these five types of indexes, the key nodes affecting the network connectivity, direct access and transfer efficiency, can be identified more accurately. Taking the public transport network of Xi’an as an example, the structural characteristics of the networks, including scale-free and small-world characteristics, were empirically analyzed. The main stops that play important roles in networks were identified based on the integrated centrality, degrees, and weight degrees. The research results showed the following: (1) Xi’an’s metro network, bus network, and metro–bus composite network all have scale-free and small-world characteristics. (2) The influence of the key stops of the metro network is concentrated, while the influence of the key stops of the bus network is scattered. (3) The public transport network in the first ring road area of Xi’an has the highest degree of direct access, and the core areas of the south, west, and north of Xi’an also have high direct access. However, the direct access in the area east of Xi’an is slightly lower. (4) Xi’an’s bus transport network covers a large area, showing the characteristics of a dual-core “central + southern” network. (5) The metro–bus composite network demonstrates a closer connection between stops and a more balanced network. (6) Finally, the degree of direct access to stops in the bus transport network and metro transport network shows the characteristics of “the single core is dominant, and the circle diffusion weakens step by step”.

Preface

GeoShanghai 2024 International Conference Volume 3: Transportation Geotechnics Shanghai, 26-29 May, 2024 GeoShanghai 2024 International ConferenceCollege of Civil Engineering, Tongji University, 2024All Rights ReservedGeoShanghai is a series of international conferences on geotechnical engineering held in Shanghai quadrennially. The conference was inaugurated in 2006 and was successfully held in 2010, 2014 and 2018, with more than 1600 participants in total. Since the last conference, the geotechnical community has witnessed many advances both in fundamental understanding and engineering practices. To demonstrate the latest developments and promote collaborations in geotechnical engineering and related fields, we launch the 5th GeoShanghai International Conference to be held in May 2024.Transportation geotechnics is a vitally interdisciplinary aspect encompassing engineering, geology, and materials science, exploring the durability, safety, and efficiency of transportation networks. Within this domain, pavement materials and structures, railroad engineering, airfield construction, and pipeline infrastructure represent the focal points. Pavements, serving as crucial interfaces between vehicles and the ground, demand attention to material properties and structural integrity. Railroads require resilient tracks capable of withstanding dynamic loads, while airfields necessitate robust pavements to support heavy aircraft. Similarly, pipelines rely on reliable foundation designs to ensure stability and mitigate risks. This topic delves into the latest advancements, challenges, and innovations within transportation geotechnics, with a specific focus on these critical themes. Through collaborative discussions and knowledge exchange, we aim to enhance the resilience, sustainability, and performance of transportation infrastructures worldwide.This volume received a total of 47 papers from countries such as China, United States, Russia, Australia, Thailand, Portugal and others, totaling 41 papers accepted. All papers were reviewed and the accepted papers will be submitted to IOP Conference Series: Earth and Environmental Science indexed by Scopus.Cordially yours,Feipeng Xiao, David Connally, Peerapong Jitsangiam, Lin Cong, Fan Gu, Yao ShanThe editors of Volume 3: Transportation GeotechnicsList of Tracks, Conference Steering Committee, International Advisory Committee, Technical Committee, Organizing Committee are available in this pdf.

Analysis of Intercity Transportation Network Efficiency Using Flow-Weighted Time Circuity: A Case Study of Seven Major City Clusters in China

Enhancing the efficiency of intercity transportation networks is crucial for sustainable regional transport development, significantly impacting travel behaviors and energy consumption. The transportation infrastructure within the city cluster is rapidly developing to accommodate the increasing traffic demand, necessitating substantial investments. It is imperative to investigate the effectiveness of intercity traffic within urban clusters, to evaluate the influence of transportation infrastructure enhancements on regional traffic efficiency. Circuity is a conventional metric used to assess the efficiency of transportation networks, primarily emphasizing distance, while overlooking factors such as travel time and traffic flow. In this study, the concept of circuity has been redefined in terms of travel time and has been referred to as the transportation network travel speed. Subsequently, the amalgamation of travel speed within the transportation network and traffic flow culminates in the proposition of Flow-Weighted Time Circuity (FWTC). Real-time intercity navigation data, offering accurate travel time estimations, are utilized to analyze the spatial distribution of intercity transport efficiency in the seven major city clusters of China, via both automobile and train modes of transportation. The results indicate that (1) as the travel distance extends, the speed of transportation within the network typically increases, albeit with increasing fluctuations, especially in the case of intercity train travel; (2) concerning the efficiency of intercity automobile travel, most city clusters demonstrate satisfactory performance, with the exception of the Guanzhong Plain. The Yangtze River Delta and Beijing–Tianjin–Heibei regions stand out for their superior performance. In terms of intercity train efficiency, the Yangtze River Delta, Beijing–Tianjin–Heibei, and Mid-Yangtze River regions exhibit higher levels of efficiency in intercity train transportation, while the Guanzhong Plain city cluster falls behind in this aspect. On the whole, the efficiency of intercity travel using automobiles surpasses that of train travel, indicating a pressing need for improvement in the latter.

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

Совершенствование транспортных технологических процессов железнодорожной сети

Purpose: To increase the efficiency of the railway transport network through the mutually beneficial interaction of lines of various categories. Methods: Methods of control theory, system analysis, synthesis and mathematical modeling were used. Results: The tools for determining the optimal location of logistics centers using the center of gravity method have been improved by taking into account the criteria for minimizing transport costs, category, specialization and social significance of the line, as well as determining the weight values of the criteria using the hierarchy analysis method. Practical significance: The proposed tools allow you to determine the location of the logistics center at the intersection of intensive and lowintensity railway lines, taking into account the criteria for minimizing transport costs, the specialization of the line and the social significance of the lines for the regions under consideration.

Tsunami hazard evaluation of river embankment structures incorporating their vulnerability to seismic strong motion

Development of coastal areas in Japan for various land uses since the 1960s has contributed to industrial upgrades and improved the efficiency of transportation networks. However, there are concerns about the vulnerability of developments on alluvial plains and reclaimed lands to geological events, like ground subsidence due to liquefaction during large earthquakes. Realistic assessment of earthquake and tsunami hazards and evaluation of possible countermeasures require accurate estimation of the amount of subsidence that can be expected from liquefaction at coastal and riverside sites supporting various structures. In this study, to evaluate the amount a river embankment structure might be expected to settle as a result of strong motion from an assumed Nankai Trough great earthquake, we conducted a numerical simulation using the soil–water coupled finite deformation analysis code GEOASIA. We then investigated the effect of the estimated embankment subsidence on tsunami inundation, which was simulated by using nonlinear shallow-water equations and a grid spacing as fine as 3.3 m. The influence of urban structures on the inundated area was taken into account by using a structure-embedded elevation model (SEM). The results showed that subsidence of river embankments and the collapse of parapet walls on top of them would increase both the depth and area of inundation caused by a tsunami triggered by a Nankai Trough scenario earthquake. Our findings underscore the importance of evaluating not only earthquake resistance but also vulnerability of coastal and riverside structures to strong motion in tsunami hazard analyses. Furthermore, the importance of tsunami inundation analysis using a SEM for predicting the behavior of tsunami flotsam in urban areas was demonstrated.

Quantifying the impacts of right-turn-on-red, exclusive turn lanes and pedestrian movements on the efficiency of urban transportation networks

Previous studies demonstrated that restricting left turning movements can enhance transportation network efficiency. However, this strategy can lead to significant increases in the volume of right-turn movements. While these right-turn movements do not conflict with opposing through traffic, they still must interact with pedestrians in adjacent crosswalks. Further, their movement is influenced by the presence of right-turn-on-red, which is commonly applied at signalized intersections to improve intersection capacity, and the presence of exclusive right-turn lanes. This paper examines the influence of these three factors (pedestrian activity, right-turn-on-red, and exclusive right-turn lanes) on vehicular operational performance at a network-wide level. Simple grid network structures are considered due to their generalizability and the performance of three network types are tested: two-way streets that accommodate left turns, two-way streets that prohibit left turns, and one-way streets. The results reveal that when there are no pedestrians, right-turn-on-red can improve the operational performance regardless of the existence of exclusive lanes, especially for the networks restricting left-turn movements, and the presence of exclusive turn lanes increases the benefits obtained by allowing right-turn-on-red. The results also suggest that allowing right-turn-on-red is more important than providing exclusive lanes when the traffic load is light; however, under heavier traffic, exclusive turn lanes become more important. The presence of pedestrians reduces overall network performance and the benefits provided by right-turn-on-red for most scenarios, as expected. This decrease in performance is larger for networks made up of two-way streets compared to those made up of one-way streets. Exclusive lanes are also found to be critical for two-way streets with left turns protected to maintain network efficiency. Overall, prohibiting left turns on two-way streets still provides the largest operational performance of all networks with these features considered.

Understanding integration issues in intelligent transportation systems with IoT platforms, cloud computing, and connected vehicles

<p>The fast improvements in information and communication technology have altered the transportation business, resulting in intelligent transportation systems (ITS) that aim to improve transportation network safety, efficiency, and sustainability. Cloud computing, networked automobiles, and IoT platforms are driving this shift. Infrastructure for current civilization must include transport. The effectiveness of transport infrastructure is vital for all countries’ individual mobility, trade, and economic development. Traffic congestion, fuel price increases, and CO<sub>2</sub> emissions have all increased in recent years, posing problems for modern civilization. It is crucial to improve transportation efficiency and safety. To develop a sustainable, intelligent transportation system, it is necessary to seamlessly integrate and interoperate with cutting-edge technologies like linked cars, the Internet of Things (IoT), and cloud computing. To respond to difficulties facing the transportation industry, which include rising fuel costs, high CO<sub>2</sub> emissions, expanding congestion in traffic, and greater dangers on the highway, integration concerns must be concluded.</p>

Assessing the Transformative Potential: An Examination of the Urban Mobility Impact Based on an Open-Source Microscopic Traffic Simulator for Autonomous Vehicles

Integrating autonomous vehicles (AVs) into urban areas poses challenges for transportation, infrastructure, building, environment, society, and policy. This paper goes beyond the technical intricacies of AVs and takes a holistic, interdisciplinary approach by considering the implications for urban design and transportation infrastructure. Using a complex methodology encompassing various software types such as Simulation of Urban Mobility (SUMO 1.17.0) and STREETMIX, the article explores the results of a simulation that anticipates the implementation of AVs through different market penetration scenarios. We investigate how AVs could enhance the efficiency of transportation networks, reducing congestion and potentially increasing the throughput. However, we also acknowledge the dynamic nature of the scenarios, as new mobility patterns emerge in response to this technological shift. Furthermore, we propose innovative urban design approaches that could harness the full potential of AVs, fostering the development of sustainable and resilient cities. By exploring these design strategies, we hope to provide valuable guidance for urban planners and policymakers as they navigate the challenges and opportunities presented by the integration of these advanced technologies.

Joint optimal pricing strategy of shared autonomous vehicles and road congestion pricing: A regional accessibility perspective

Current studies on shared autonomous vehicles (SAVs) and road congestion pricing (RCP) mainly focus on their independent effect on mobility, and rarely consider the joint impact of the two policies on accessibility. In light of this incentive, this paper establishes a bi-level programming model with multi-objective functions and multi-decision variables to solve the joint optimal pricing problem of SAVs and RCP considering accessibility. Because of the complex relationship between the decision variables and the objective functions, the proposed bi-level programming model belongs to a black-box problem. A machine learning algorithm based on multi-objective Bayesian optimization was developed to solve the proposed model, by taking Jiangyin City as an empirical case study. The results show that the combined implementation of SAVs and RCP plays an essential role in enhancing the performance of land-use and transportation systems. However, this advantageous outcome relies on an appropriately appropriate pricing strategy. Failing to account for the influence of the pricing strategy on implementation efficacy, SAVs and RCP could result in a 22.2 % and 5.0 % degradation in regional accessibility and total flow time, respectively. In contrast, the joint optimal pricing strategy of SAVs and RCP can not only mitigate traffic congestion and improve transportation network efficiency but also optimize urban spatial distribution and promote compact urban development, ultimately achieving a 14.0 % improvement in regional accessibility and 18.5 % reduction in total flow time. Sensitivity analysis reveals that with the increase in SAV market penetration rate, regional accessibility sees a steady rise. Conversely, the total flow time initially escalates before experiencing a decline. The method proposed in this paper serves not only in determining the joint optimal pricing strategy of SAVs and RCP to enhance accessibility, but also in analyzing and optimizing the accessibility impact of other land-use and transportation policies.

Optimized Flexible Lane Assignment Using Simulation and Metaheuristic Algorithms

Technological advances with connected and autonomous vehicles (CAVs) have opened opportunities to increase the efficiency of transportation networks. A novel control framework called combined flexible lane assignment and reservation-based intersection control (CFLARIC) system has been recently proposed for better management of directionally unrestricted CAVs traffic flows in an urban environment. CFLARIC offers a full spectrum of lane assignment possibilities in combination with the appropriate reservation-based intersection control. In CFLARIC, vehicles position themselves in a proper lane before they reach the downstream intersection, which enables resolution of vehicular conflicts both between intersections and within the intersection boxes. Although CFLARIC has shown promising results, only a limited number of lane assignment scenarios have been designed and tested using some preset rules and based on the previous research objectives. Therefore, the optimality of their performance has never been studied. The objective of this study is to address the flexible traffic lane assignment in such a system as a network optimization problem, in which an optimal lane assignment schema is achieved using metaheuristic optimization algorithms. To this end, a combination of NetLogo and the BehaviorSearch tool is utilized for the simulation modeling and optimization process using random search and brute-force as the search algorithms. The output of the optimization process is the lane assignment that leads to a minimum total travel time for a given network geometry and traffic volumes. Results indicate that a flexible control concept such as optimized CFLARIC has great potential to improve the efficiency of traffic control strategies with CAVs.

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

Artificial Neural Networks-Based Route Selection Model for Multimodal Freight Transport Network During Global Pandemic

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management.

The relation between walkability in hot arid regions and the built environment: the case of Kuwait in the Arabian Gulf

ABSTRACT Walkability is a key indicator of urban quality, especially in hot arid regions where it is often discouraged by harsh climatic conditions and car-oriented development. This work examined the effect of built environment on walkability in hot arid regions, taking Kuwait as a case study. The number of minutes people spent walking had heavily declined over the past century. In Kuwait, the situation is even more prevalent due to the lack of efficiency of the public transport network. This had resulted in more than 90% of the trips being completed by private vehicles. Perceptions and preferences of pedestrians toward walkability were surveyed. Walkability in residential areas was assessed to understand the effect of built environment on walking activities. Results showed that Qusour area had the highest walkability index. Correlation results, assessed using SPSS, showed that car availability had an impact on going for walks.

Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study.

Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.

Data-Driven Multi-Criteria Assessment Framework for Analyzing the Reliability of Bus Services

Intelligent systems have been extensively used to improve the reliability of transport services as a result of technological advances. Despite the technical and methodological achievements, public transportation companies are still facing excessive challenges in assessing the performance and reliability of the system. This study establishes a data-driven multi-criteria decision-making model for prioritizing bus routes that illustrates both operator and consumer views on bus routes. The multi-criteria fuzzy outranking process is handled by ELECTRE III and Condorcet methods. The developed model utilizes alternative indices of bus travel-time reliability to fully capture the uncertain nature of the input data. The reliability assessment framework is based on automatic vehicle location (AVL) data which works as an effective evaluation system for enhanced service reliability on different routes network-wide. Using this model, bus transport companies can set a benchmark and a reliable ranking system for their bus routes. This hybrid prioritization framework is used for characterizing and enhancing transport network efficiency. The effectiveness of the model is examined by quantifying the reliability of eight bus routes controlled by the Qazvin public transportation system, in Iran. A wide range of AVL data sources is employed within an in-depth statistical analysis based on both user and operator preferences. According to the concordance matrix results, line 18 has been found to be superior to other bus routes, and the possibility of identifying less efficient bus routes has been fulfilled.

Examining the relationship between socioeconomic structure and urban transport network efficiency: a circuity and spatial statistics based approach

Examining the relationship between socioeconomic structure and urban transport network efficiency: a circuity and spatial statistics based approach

Stochastic-Geometry-Based Analysis of Multipurpose UAVs for Package and Data Delivery

Using drones for communications and transportation is drawing great attention in many practical scenarios, such as package delivery and providing additional wireless coverage. However, the increasing demand for unmanned aerial vehicles (UAVs) from industry and academia will cause aerial traffic conflicts in the future. This, in turn, motivates the idea of this article: multipurpose UAVs, acting as aerial wireless data relays and means of aerial transportation simultaneously, to deliver packages and data at the same time. This article aims to analyze the feasibility of using drones to collect and deliver data from the Internet of Things (IoT) devices to terrestrial base stations (TBSs) while delivering packages from warehouses to residential areas. We propose an algorithm to optimize the trajectory of UAVs to maximize the size of collected/delivered data while minimizing the total round trip time subject to the limited onboard battery of UAVs. Specifically, we use tools from stochastic geometry to model the locations of the IoT clusters and the TBSs and study the system performance with respect to energy efficiency, average size of collected/delivered data, and package delivery time. Our numerical results reveal that multifunctional UAVs have great potential to enhance the efficiency of both communication and transportation networks.