领域：学术论文 · 应用学科

Identified Transportation Challenges
1. Technical Capability and Real-Time Data Utilization
Several studies highlight critical issues related to technical capability and real-time data utilization in smart transportation systems. The main issue is the integration of heterogeneous IoT data sources, which often creates data silos and interoperability problems that hamper seamless communication and decision-making [27,28,29,30,43]. Additionally, many legacy systems struggle to process dynamic, real-time data, resulting in suboptimal traffic management and extended response times [30,35,36,37,41,45,46]. Researchers further note a lack of robust predictive models, which undermines short-term traffic flow predictions and hinders efficient management of congestion in response to factors such as weather, road incidents, and special events [29,30,32,33,42,48,49].
Limited integration of multimodal traffic data with situational context also impedes effective transportation planning, underscoring the need to incorporate contextual information (e.g., real-time traffic restrictions and urban planning changes) [33,41]. Data reliability and accuracy remain pressing concerns, with sensor anomalies necessitating rigorous validation to ensure data quality [34,38,39,46,47]. Finally, interoperability gaps across transportation subsystems (e.g., buses and subways) create operational inefficiencies and negatively affect commuter experiences [28,33,45].
2. Traffic Congestion
Traffic congestion remains a central challenge in urban environments, impairing economic efficiency, environmental sustainability, and public health. Primary contributors include escalating vehicular density—driven by urbanization and rising vehicle ownership—and infrastructure limitations that fail to accommodate growing travel demand [24,26,28,35]. Inadequate real-time data processing curtails proactive congestion management measures, such as adaptive traffic signals and dynamic routing, while inefficiencies in traffic management systems further exacerbate the problem [27,34,43]. Severe congestion at key locations and during peak hours highlights the challenges of an overburdened transportation network that lacks the flexibility to accommodate fluctuating demand [31,36]. Long queues at intersections and inadequate signal timing contribute to further delays and inefficient traffic flow management [36].
Congestion also disrupts travel time reliability and impacts public transportation systems, particularly buses sharing mixed traffic lanes, leading to service irregularities and passenger dissatisfaction [42,44]. Beyond operational inefficiencies, the environmental impact of sustained congestion is significant, including higher fuel consumption and emissions that degrade air quality and contribute to respiratory ailments [38,48]. These findings stress the urgency of implementing measures that mitigate congestion and foster sustainable transportation alternatives.
3. Public Transit and Multimodal Transportation Systems
Public transit and multimodal systems face interconnected challenges that restrict efficiency, accessibility, and overall effectiveness. Inequitable access is particularly acute in rural areas, where limited mobility options constrain access to essential services and exacerbate social inequalities [25]. This challenge is compounded by rising operational costs for demand-responsive transport systems and the difficulty in balancing multiple objectives, such as cost, coverage, and equity [25]. Operational inefficiencies, such as static timetables and unpredictably fluctuating traffic conditions, extend passenger wait times, reduce service reliability, and increase dissatisfaction [28]. Furthermore, a lack of integrated multimodal data hampers seamless transitions among various travel modes, while suboptimal passenger flow management during peak hours compounds congestion and service quality issues [33,45]. Consequently, planners seek integrated strategies to enhance network design, service frequency, and data sharing, ensuring that transit systems remain accessible, efficient, and adaptable to changing user demands [45].
4. Traffic Safety and Accidental Risks
Traffic safety and accidental risks remain substantial concerns in both conventional and increasingly connected or automated transport systems. Recurring traffic violations—such as drunk driving and red-light running—persist despite current enforcement, indicating an unmet need for integrating violation data into accident prevention measures [49]. The lack of advanced sensor deployment in accident-prone areas further limits the capacity to identify and address these violations proactively, leading to persistent safety risks [49]. Security threats in vehicular networks are another pressing concern, as increased connectivity in smart transportation systems introduces vulnerabilities to cyber intrusions and attacks, which can compromise vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, posing substantial risks to road users and critical infrastructure [43]. The need to balance mobility with health restrictions during crises, such as pandemics, presents unique challenges. Restrictions, including curfews and changing alert levels, disrupt traffic flow and necessitate real-time adjustments to route planning, complicating efforts to ensure both public safety and transportation efficiency [39]. Additionally, in the era of autonomous vehicles (AVs), safety risks in mixed-traffic environments may arise due to the coexistence of autonomous and human-driven vehicles, leading to unpredictable interactions and potential conflicts caused by differing driving behaviors and decision-making processes [37]. AVs operate based on pre-programmed algorithms, whereas human drivers rely on intuition and experience, increasing the likelihood of accidents in complex urban environments [37].
5. Environmental Impacts
Environmental concerns tied to transportation systems pose major barriers to sustainable urban development. Persistent congestion intensifies emissions of harmful pollutants, such as nitrogen oxides and particulate matter, degrading air quality and urban livability [38]. Reliance on fossil fuels contributes significantly to greenhouse gas emissions, climate change, and economic burdens associated with fuel consumption [44]. Poorly optimized logistics and traffic management practices exacerbate these challenges by increasing both travel distances and fuel consumption [47].
In response, numerous studies highlight a shift toward green transportation solutions, including enhanced public transit, active travel modes (e.g., walking and cycling), and smart mobility technologies [44]. Such strategies aim to lower environmental footprints and foster sustainable urban growth [44].
6. Transportation System Operation and Management
Effective operation and management of transportation systems are essential for ensuring efficiency, reliability, and sustainability. Nonetheless, resource limitations, legacy systems, and growing complexity create substantial operational hurdles. Studies point to constrained resources and the difficulty of balancing cost, coverage, and equity objectives, particularly in densely populated urban settings [25]. Conventional traffic management strategies are increasingly inadequate for handling modern, complex urban mobility, particularly as vehicle density rises [26,35]. One major contributor to inefficiency is the inadequate processing of real-time traffic data, which hinders the ability to implement adaptive traffic control measures [35]. Traffic light operations, in particular, remain inefficient, failing to respond effectively to fluctuating traffic volumes and dynamic demand patterns [34].
Furthermore, many systems are ill-equipped to incorporate real-time data and respond adaptively to fluctuating traffic conditions [36]. Traditional predictive models, which are typically designed for static or historical data, often prove ill-suited or exhibit inadequate performance when applied to dynamic, real-time traffic conditions. The complexity and unpredictability of streamed data can lead to delayed or suboptimal traffic flow management and inefficient resource utilization [44]. Freight operations add further intricacy, as route planning, resource allocation, and fleet management must account for cost, environmental impact, and service reliability [47]. Collectively, these findings illustrate the urgent need for data-driven, integrated strategies capable of addressing congestion, resource allocation, and technological interoperability challenges.

翻译考点：
考点1：heterogeneous IoT data sources 需译为 异构物联网数据源，IoT不可不译。
考点2：legacy systems 可译为 “传统系统”或“遗留系统”。
考点3：multimodal traffic data 可译为 “多式联运交通数据” 或 “多模态交通数据”。
考点4：Multimodal Transportation 需译为 多式联运。
考点5：vehicle-to-vehicle (V2V) 需译为 车对车（V2V）。
考点6：vehicle-to-infrastructure (V2I) 需译为 车对基础设施（V2I）。