Journal of Transportation Engineering, Part A: Systems

Public–private partnerships (PPPs) and sustainability have received considerable attention in delivery and execution of transportation infrastructure projects in the United States. Existing sustainability rating tools are neither designed nor suitable for PPPs due to unbalanced weights among the triple bottom line (TBL) of sustainability and the lack of additional considerations beyond the TBL. This study proposes and validates an integrated sustainability assessment framework that aims to capture all of the relevant and important decision factors in terms of assessing the sustainability of PPP highway projects in a comprehensive manner. Through literature review, semistructured interviews, and questionnaire surveys featuring Delphi and analytic hierarchy process techniques, a list of applicable assessment factors (five dimensions, 18 indicators, and 65 subindicators) and their respective weights and priorities were identified. Based on these assessment factors, the proposed framework seeks to determine PPP projects’ level of achievement in terms of sustainable practices. The framework was validated for its applicability and usability for US PPP highway projects through an online survey. Results show that the economic/financial dimension is the most important dimension for highway PPP experts, followed by contractual, technical, environmental, and social dimensions in descending order by relative importance. This calls for additional attention to be paid to environmental and social sustainability considerations in PPP highway projects. Meanwhile, the results also reflect an urgent need for improved sustainability planning and awareness among stakeholders of PPP highway projects.

The management of transportation infrastructure, particularly urban road networks, faces significant challenges due to limited financial resources. To overcome this hurdle, many have turned to public-private partnerships (PPPs) to enhance the economic value of transportation infrastructure outputs. However, monetizing the value of urban road networks and their surrounding areas remains a complex issue. Urban road networks are complex and vast systems, requiring a comprehensive approach to assess their true value accurately. This study proposes a novel model aimed at monetizing the assets of urban road networks. Unlike previous approaches that solely focus on physical values, our methodology also considers social values in relation to the performance, and spatial importance of urban road networks. The model employs the asset replacement cost as the foundational component and incorporates unquantifiable factors that contribute to economic externalities. Additionally, it takes into account the physical condition of the roads, allowing for the depreciation of road assets. Furthermore, we investigate the impact of social values on roads, a crucial aspect often overlooked in existing studies. This comprehensive approach was demonstrated through a case study in a city in China, where a section of the urban road network was selected for evaluation. The results showcased the model’s effectiveness in evaluating the assets of each link in the urban road network. Moreover, it revealed the evolution pattern based on the spatial distribution of various assets and their influence on the overall asset valuation of the road network. The proposed methodology also provides a theoretical foundation to transition from road infrastructure management to asset management. Beyond its academic contributions, our model offers valuable insights for decision-makers, enabling investment analyses and trade-offs in PPPs aimed at enhancing road network assets. By utilizing this approach, stakeholders can make informed decisions to optimize the economic benefits of urban road infrastructure and ensure sustainable transportation systems for the future.

Energy saving has become a key concern of metro systems to reduce operating costs and meet the requirements of sustainable development. Energy-efficient timetabling, an effective way to reduce traction energy consumption, has gotten much attention in recent decades. However, limited research about energy-efficient timetabling simultaneously considered realistic speed profiles and the influence of train loads, which change greatly in different sections and significantly affect energy consumption. Therefore, this paper proposes an energy-efficient timetabling approach to improve regenerative energy utilization based on varying train loads and realistic speed profiles for bidirectional metro lines. First, based on the operational and safety constraints, this paper constructs an integer energy-efficient timetabling model where dwell times and nonuniform headways are taken as decision variables. Besides, the turnaround process is also considered to ensure rolling stock circulation efficiency. Sectional passenger volumes are employed to determine the varying train loads. Energy consumption is calculated based on realistic speed profiles. Owing to the complexity of the model, an improved adaptive large neighborhood search (ALNS) algorithm is designed to solve this model according to its characteristics. Finally, numerical examples are conducted based on the practical data from Shanghai Metro Line 17. The results indicate that the proposed approach effectively solves the energy-efficient timetabling problem with varying train loads and realistic speed profiles for bi-directional metro lines. The approach is suitable for off-peak hours and can reduce energy consumption effectively, and the reduction is up to 11.9% for the presented cases. The improved ALNS algorithm can find good solutions within reasonable times in different cases. Moreover, compared with the widely used genetic algorithm in the literature, the improved ALNS algorithm is more efficient in solving the proposed energy-efficient timetabling problem in different cases.

Abstract

Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement.

Practical Applications

The HCM serves as a fundamental reference guide to assist traffic engineers in analyzing and evaluating multimodal operations on various types of roadways. The implementation of performance evaluation methodology of urban street facilities for motorized vehicles in the HCM relies on a large amount of traffic volume collections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments, which can become a barrier against the efficient use of the HCM. Further, the use of alternative tools remains to be explored to supplement and facilitate the HCM calculation framework. Therefore, this research is focused on utilizing vehicle trajectory data as an alternative tool to implement and assess the existing performance evaluation methodology of urban facilities for motorized vehicles in the HCM 7th Edition. The findings from this research aim to facilitate and expand the use of HCM for practitioners in arterial operational performance evaluation and offer recommendations for further performance evaluation methodology development and enhancement in the HCM.

Abstract

In order to increase the online vehicle velocity prediction (VVP) strategy’s forecast performance, an adaptive VVP strategy considering traffic signals is presented for multiple scenarios. Initially, the algorithm of a general regressive neural network (GRNN) paired with data sets of the ego-vehicle, the vehicle in front, and traffic lights is used in traffic scenarios, which increasingly improves the prediction accuracy. To ameliorate the robustness of the algorithm, then the strategy is optimized by particle swarm optimization (PSO) and k-fold cross-validation to find the optimal parameters of GRNN in real-time, which constructs an adaptive online PSO-GRNN VVP strategy with multi-information fusion to adapt with different operating situations. To verify the proposed strategy, traffic scenarios are established inside the co-simulation environment. The adaptive online PSO-GRNN VVP strategy is then deployed to a variety of simulated scenarios to test its efficacy under various operating situations. Finally, the simulation results reveal that in urban and highway scenarios, the prediction accuracy is separately increased by 31.3% and 48.3% when compared to the traditional GRNN VVP strategy with fixed parameters utilizing only the historical ego-vehicle velocity data set.

Practical Applications

Energy problems in today’s world are becoming more and more prominent: fossil energy is in short supply, and excessive carbon emissions aggravate the greenhouse effect. In this context, the new energy vehicle industry has developed rapidly. Hybrid cars are a very popular vehicle, because it is affordable, free from range anxiety, energy efficient and environmentally friendly. In a hybrid car, there is a small module that always calculates how much energy the engine and the battery need to output each time the accelerator is stepped on. This paper uses the traffic information to help improve the accuracy of online vehicle velocity prediction in the short-term future, and the adaptability of this prediction algorithm to dynamic working conditions. Thus, this algorithm provides accurate information for the energy distribution module to achieve the lowest energy consumption in a journey. Combined with our research results, hybrid cars can predict the short-term future velocity in real time during operation, and automatically adjust the energy distribution ratio, so that when we drive a hybrid car from home to work, no matter whether the road conditions are congested or smooth today, we can all achieve more fuel savings, thereby reducing the cost of use and carbon emissions.

Decentralized traffic signal controls, such as max-pressure (MP) control, also known as back-pressure (BP) control, have received increased attention recently. MP signal control has been analytically proven to maximize the network throughput and stabilize vehicle queue lengths whenever possible. However, previous work on MP signal control with cyclic and noncyclic phases did not include pedestrian access, which may increase pedestrians’ travel time, and delay or even encourage some dangerous behaviors like jaywalking. Because the movement of pedestrians is a nonnegligible factor in traffic management, and many urban planning researchers have found that walking space and walking continuously have significant health, safety, and environmental impacts, a pedestrian-friendly MP signal control policy is needed. Here, we propose a novel pedestrian-friendly MP signal control, Ped-MP, that considers pedestrian access in an urban network to achieve both maximum stability for private vehicles and a comfortable, safe walking experience. This study modifies the original MP control to include pedestrians’ access for the first time. Furthermore, this policy still inherits the decentralized property of original MP control, which means it only relies on the local information of individual intersections. Simulation studies are implemented on a popular benchmark network, the Sioux Falls network, with added pedestrians network. The results indicate that, although considering pedestrians’ access may reduce the stable region for vehicles, the pedestrians’ travel time and delay can be reduced significantly.

Despite the needs of assessing the time-varying queue length for incident traffic management, such imperative information has not yet been available in practice, due partly to the lack of reliable sensor data on many highways plagued by nonrecurrent congestion. Hence, this study first presents an incident queue impact assessment system that allows highway agencies to perform reliable estimate of the queue distance during the incident clearance period with available detector data, and then discusses two alternative models for supporting approximating such impacts on roadway networks without traffic sensors. The proposed system’s major model, grounded in the classical shockwave theory, has augmented the formulations for queue propagation dynamics with the impacts from incoming drivers’ perceptions and responses to the progressively constrained traffic conditions. To cope with the lack of real-time traffic flow information, the first alternative model integrates the increasing available speed data from probe vehicles or other sources with lane blockage patterns to approximate the discharge flow rate of the incident. The second alternative model centers on the methodology and calibration methods for constructing a set of speed–flow rate relationships for different times of day with archived traffic data to approximate the arriving flow rate. With such information, responsible agencies can then proceed the approximation of incident queue variation length and take necessary actions. Performance evaluation of the proposed system with both field incident data and simulated scenarios has confirmed its promising properties, especially for highway networks without reliable traffic surveillance systems.

Vehicular flow rate is an essential measure commonly collected by inductive-loop detectors for transportation agencies to evaluate freeways and highways. Loop detectors are typically located in urban areas due to installation and maintenance costs, and do not provide large spatial coverage. Crowdsourced data provide large spatial coverage, but typically do not capture vehicular flow rates. Therefore, a dynamically weighted ensemble (DWE) comprised of XGBoost and neural network models is proposed to expand the spatial coverage of vehicular flow rates by estimating flow rates for the Phoenix, AZ, metropolitan area using crowdsourced data. The model is evaluated using K-fold cross-validation methods, achieving a cross-validated mean absolute percent error of 21.74%, outperforming all other comparison models. The trained model is then used to estimate vehicular flow rates along highways and freeways throughout the state of Arizona. The proposed method provides transportation professionals with a transferable, cost-effective solution for large-scale flow rate estimation.

Rear-end collisions between autonomous vehicles (AVs) and human-driven vehicles (HVs) represent critical scenarios in road networks. Few studies have focused on the scenarios where a HV hits an AV from behind (called a HV-AV collision). This paper aims to investigate the occurrence of HV-AV collisions in the stop-in-lane (SiL) scenario where a HV follows an AV. A humanlike brake control (HLBC) model is firstly proposed to simulate the driver brake control. The HLBC model considers human driving intention, vision-based expectancy, and certain inherent characteristics of human driving to achieve dynamic humanlike braking. Additionally, the joint distribution of off-road-glance and time-headway is originally introduced to simulate the glance distraction of drivers during their dynamic vehicle control. Sequentially, we apply the HLBC model to the SiL scenario to investigate how the HV-AV collision probability changes with respect to various dynamic driving parameters. The results of the case study provide a thorough understanding of the dynamic driving conditions that lead to HV-AV collisions and pave the way for identifying practical countermeasures to improve the road safety involving AVs.

Abstract

In Brazil, roadway intersections are designed based on the precepts contained in manuals prepared by official government agencies and documents such as the Highway Capacity Manual (HCM). However, some types of intersections, termed alternative intersections, are not included in these documents, such as elongated roundabouts (ERs). This study proposed a method to estimate the level of service of this type of intersection located on two-lane rural highways. For this purpose, traffic data were collected in the field to obtain diverse traffic patterns and to characterize the desired speeds at sites with ERs. The Aimsun Next traffic simulator was used to create several hypothetical scenarios, which allowed the development of models to estimate the chosen performance measures for ER analysis (the average delay, $d_i$, and extra distance travel time, EDTT) as a function of traffic and geometry parameters. The validation of the proposed method was performed, and it was observed that the traffic models produced values of $d_i$ and EDTT close to those found in the field.

Practical Applications

The results obtained in this work allow the reader to evaluate the operational quality of an intersection constantly implemented in Brazil, an elongated roundabout (ER). No methods were found in the national and international literature aimed exclusively at this type of intersection, which justifies the need for this work. The multilinear models that make up the method serve to estimate performance measures aimed to evaluate the ERs, the average delay, $d_i$, and the extra distance travel time, EDTT. To estimate these two performance measures, the user of the proposed method needs to obtain, around the ERs, simple information, such as the traffic flow of the primary road; the traffic flow of the secondary road; the free flow speed; and the percentage of heavy vehicles. The method can reproduce values of these performance measures close to the data collected in the field, considering the conditions observed on Brazilian highways. This may indicate that the method could also be applied in other countries if the proposed multilinear models are appropriately adjusted based on local traffic data.

This work aims to investigate the sensitivity to disturbance for the bidirectional multiple-vehicle following (BDM) V2V vehicular platoon. A disturbance term is introduced into the control input, and a closed-loop equation about the spacing error is derived. The sensitivity of error propagation to disturbance is defined based on the error transfer function matrix, and the sensitivities to external disturbance and the motion of the leading vehicle have been analyzed in the frequency domain and validated by model simulations. The affecting factors, including communication topology, control gains, and weights for acceleration communication, have been analyzed. It is revealed that the communication topology plays a significant role in reducing the sensitivities of error propagation to disturbance, and the effects on the sensitivity to external disturbance and the motion of the leading vehicle are roughly opposite. The spacing control gain plays a more important role in reducing the sensitivity compared with the speed following control gain, especially for the sensitivity to external disturbance. More weight should be distributed to vehicles that are relatively nearer to the current vehicle for the purpose of reducing the sensitivities.

The line-based hybrid transit (LHT) system is a public transit mode that improves transit performance by integrating the fixed-route transit service with a demand-responsive transit service. To facilitate the implementation and operation of such a hybrid transit system, this paper analyzes and compares the operating performance of the LHT system in two different road network structures: a rectangular structure and circular structure. To conduct a meaningful comparison, the generalized cost of LHT systems for both structures is formulated using the same analytical framework based on the continuous approximation approach. After constructing various time cost indicators related to system performance under two structures, the optimal design model is formulated as a nonlinear program, and an iterative enumeration method with vectorized programming is designed to solve it. A case study is performed to compare various cost components corresponding to the two structures, and sensitivity analysis is conducted. The results suggest that the LHT system is more suitable for operating in rectangular structures under low demand densities, while the system in circular structures is more suitable for operating under high demand densities. However, the performance of the LHT system in a circular structure is superior to that in a rectangular structure at most demand density levels for testing scenarios with high service area size or value of time.

Analyzing the travel mode choice behavior of residents within the catchment area of a rail station holds significant importance in optimizing the transportation structure and establishing a green transit–oriented system. This study collected travel survey data from residents living within the rail station catchment area in Nanjing, China. A multilevel indicator and multiple causes (MIMIC) model was constructed to analyze potential psychological factors influencing residents’ choice of travel mode. The cross-nested logit (CNL) model was employed to reveal differences in residents’ travel intentions and preferences for various modes of transportation. The results show that the convenience and comfort of rail transit are paramount to residents, and their perceptions of external barriers in the area proximate to the rail station directly shape their travel intentions and choices. Factors such as travel attitude, preference, subjective norms, and external obstacles all exert a positive influence on residents’ mode choice; preference exerts the least impact, whereas environmental perception is the most determinative.

This study introduces a novel multilevel disruption detection method for road networks. The proposed monitoring and disruption detection method can detect disruptions at both the network and road-segment levels simultaneously. The monitoring process begins with a short-term prediction of hourly traffic speed on each road segment of the network using long short-term memory (LSTM) artificial neural networks. The prediction errors on each road segment at each timestep are used as a proxy to detect disruptions. Network-level disruptions are detected using a multivariate cumulative sum (MCUSUM) control chart. Local disruptions at a road-segment level of granularity are detected by decomposing the monitoring statistic of the MCUSUM control chart that follows a quadratic form using the correlation-maximization (corr-max) transformation. The proposed method was applied to the road network of Manhattan in New York City to examine its performance in detecting disruptions caused by Hurricane Sandy in 2012. The outcomes indicated that the proposed method could detect disruptions precisely at both network and road-segment levels. Whereas existing solutions can either monitor the entire network as a whole or focus on one or a limited number of road segments, the proposed method in this study can recognize if the entire network has been disrupted and also can recognize the road segments that are experiencing unusual traffic patterns. The outcomes of this study set the stage for transportation agencies and decision makers to design adaptive traffic management systems using real-time disruption detection at the network and road-segment levels.

Abstract

Timing traffic signals using coordination zones of one to three signalized intersections was tested as an approach for reducing speeding opportunities on urban arterials. Short coordination zones can reduce speeding opportunities because they allow cycle length to be shorter at most intersections, with less excess arterial green time during which vehicles can pass through unconstrained; they also avoid the outcome, common to long coordination zones, of having large clusters of intersections with simultaneous green, which create an incentive to speed. In a case study of Boston’s Huntington Avenue performed using microsimulation, existing coordinated control over a stretch of nine intersections was compared with control short coordination zones with one to three intersections per zone, with each zone’s cycle length tailored to the needs of its intersections. Speeding opportunities per hour—defined as the number of vehicles entering an intersection on a stale green and with no vehicle ahead of them for at least 5 s–fell by 46%–51% midday and by 24%–33% in the AM peak, depending on the base of comparison, while vehicle delay was unchanged in the AM peak and increased by only 9% midday, and average pedestrian delay crossing the arterial fell substantially.

Practical Applications

Cities and citizens alike are concerned with improving safety and livability by controlling traffic speed on arterial roads. An approach is presented for reducing speeding on multilane arterials by using traffic signal timing to reduce speeding opportunities and incentives. Because the experiment was conducted in a simulation environment, evidence cannot be determined regarding actual speeding behavior; however, evidence is presented for how the approach reduces speeding opportunities in comparison with conventional approaches for arterial traffic signal timing, together with delay reductions to pedestrians and little or no impact on vehicle delay.

Horizontal curves on undivided hilly roads are critical segments prone to head-on crashes between opposite-direction-bound vehicles. The majority of head-on crashes occur due to the inability of drivers to keep their vehicles at right lateral placements across the carriageway when maneuvering on these curves. The traffic safety assessment at horizontal curves conducted in past studies was largely based on previous crash records. However, there is a complete absence of a proximal indicator quantifying the crash potential of a horizontal curve, unlike other critical road segments. As background, this paper conceptualizes Mutual Area in Lateral Distribution (MALD) as a proximal indicator for head-on crash potential at horizontal curves and demonstrates an effective methodology for its field estimation. Theoretically, MALD may vary within a range of [0–1] with a higher value indicating a higher head-on crash potential. Further, a validation test was performed where the proposed methodology was implemented to determine the MALD of seven distinct horizontal curves. The results justified the effectiveness of MALD as a proximal indicator of head-on crash potential.