Optimization-based tools have been included in many engineering management systems for individual infrastructure asset classes such as pavement management systems (PMS) and bridge management systems (BMS). These tools typically include single-objective optimization analyses. However, real-world decisions concerning asset preservation and renewal often involve more than one objective reflecting the various goals of the agency and need to evaluate possible alternatives according to multiple criteria. Traditional single-objective optimization approaches for handling such situations optimize a selected most important objective while either neglecting the less important competing objectives or imposing them as known constraints in the optimization formulation. This approach often does not provide truly optimal solutions. Multiobjective optimization formulations have clear theoretical advantages but increase the complexity of the mathematical formulation. This paper presents a review of the application of multiobjective optimization techniques in various working levels of highway asset management. Some promising techniques for the different infrastructure management functions are identified, and relevant characteristics are summarized and compared. Based on the applications reviewed, it can be concluded that multiobjective optimization could be effective for supporting many infrastructure management business processes. The review also suggests that a synergistic integration of complementary techniques may help develop practical and efficient decision-supporting tools that take advantage of the benefits and avoid potential drawbacks of the individual techniques.

Bus priority strategy has widely been recognized as an effective countermeasure in the practical field of urban traffic congestion mitigation. This study proposes a bi-level programming model to comprehensively analyze the combinational optimization problem of exclusive bus lanes (EBLs) with variable bus frequencies (BFs) in multi-modal transportation networks. The objective function is to minimize the sum of the road users’ travel costs and the transit operating costs. The upper level model is an optimal decision-making program for setting up EBLs and BFs, and the lower level is a multi-modal transportation network equilibrium model. An efficient genetic algorithm is designed to solve the problem. The results of the numerical analysis show that the combinatorial optimization scheme of EBLs and BFs performs well with regard to the objective function in the model. The outperformance of combinatorial optimization scheme becomes greater with increased traffic demand, whereas oversetting of EBLs can result in reduced operating efficiency of the transportation system.

The development and decreased cost of technology and communications have brought about a huge increase in the availability of traffic data. With every passing day, traffic management centers must deal with an increased amount of detailed data. Once the real time use of these data is complete, they must be stored for long periods of time. In this long-term context, the vast amount of raw data is meaningless, which is a clear example of data asphyxiation. Traffic management centers must aggregate and synthesize the data to extract the maximum information from them. Pattern classification is a way to deal with this issue. Traditionally, traffic demand patterns have been easily constructed using ad hoc methods, where the experience and judgment of the analyst are their main attribute. These procedures lack the required rigor to support current needs in terms of planning and operational management. This paper proposes a quantitative method to systematically derive traffic demand patterns from historical data. The method is based on the cluster analysis technique and allows for the inclusion of preexisting knowledge, which eases the interpretation and practical use of the results. The proposed pattern classification procedure is applied to 5 years of hourly traffic volumes on a Spanish highway. The obtained results prove the validity and utility of the method in accurately summarizing the seasonal and daily characteristics of traffic demand.

Characteristics of mixed traffic on a road with a signalized crosswalk are investigated by cellular automaton modeling. The interaction mechanism between vehicles and pedestrians is taken into account. Also, the inordinate behavior of vehicles near the crosswalk, and various behaviors of three types of pedestrians (i.e., careful pedestrians, normal pedestrians, and riskers), are incorporated into the proposed model. By using numerical simulations, the case for pedestrian flow consisting of careful and normal pedestrians is investigated. Phase diagrams and spatiotemporal diagrams are presented to show different traffic states. The results show that there is a critical value that divides the vehicle flow into a free flow portion and a congested flow portion. Interestingly, there are two critical values for pedestrian flow for a large signal cycle value, whereas there is only one critical value when the signal cycle value is smaller than a critical value. Finally, the variation of violating frequency against time during a signal cycle is investigated. It is found that there are two local maximum values on the frequency curves when the pedestrians consist of all three types, and this phenomenon is coincident with the qualitatively real traffic.

A microsimulation approach is presented for assessing safety at unsignalized intersections using critical traffic conflicts computed in simulation models as a surrogate safety measure. The proposed approach was applied to a crash data set, collated over a 5-year monitoring period, corresponding to urban unsignalized intersections of the city of Salerno, Italy. Given that over 45% of accidents recorded at these intersections occurred more especially in six 1-h time periods during the day and that these hourly periods corresponded to the peak traffic volumes, it was considered worthwhile to investigate each of these 1-h concentrated time intervals. Traffic flows were computed by using video cameras placed at each intersection and expressed as peak-hour volumes in the analysis. A process of microsimulation model calibration and validation was carried out, and a good level of conformity between the traffic simulated in the peak hours and the corresponding one measured in the field was obtained. Subsequently, a form of software, which is compatible with the applied microsimulation software, was used for identifying the number of critical conflicts at the intersections investigated. For this aim, a collision was assumed to be very probable when conflicts are characterized by a time to collision (TTC) and post-encroachment time (PET) below the threshold values of 1.5 and 5 s, respectively. The relationship between critical conflicts computed in microsimulation models and actual crashes was found to be statistically significant. A crash-prediction regression model was then developed for the investigated intersections, which allows predicting crashes in the field on the basis of the critical conflicts computed in simulation. The proposed model was found to fit the accident data slightly better than the traffic volume-based regression model.

Heavy vehicles and passenger cars differ in their maneuverability and acceleration capabilities. Heavy vehicles thus influence other traffic in a different manner than passenger vehicles, causing different levels of traffic instability. Increasing number and proportion of heavy vehicles in the traffic stream may result in quite different traffic flow characteristics. Over the last five decades many studies have investigated passenger car drivers’ car-following behavior. However, the existence of heavy vehicles in the traffic stream has not received the same attention. This paper investigates the different car-following behavior of drivers in congested heterogeneous traffic conditions. It specifically focuses on the existence of heavy vehicles in general traffic and their interaction with other vehicles. Four different combinations of passenger car and heavy vehicle were considered. These combinations include passenger car-following a passenger car, passenger car-following a heavy vehicle, heavy vehicle following a passenger car, and heavy vehicle following another heavy vehicle. A data set from a freeway in the United States was used to show the different car-following behavior of drivers for each combination. This study analyzes space and time headways, drivers’ reaction times, and vehicle accelerations. It also introduces different car-following thresholds for each car-following combination. It was found that the presence of heavy vehicles causes larger space and time headways, longer reaction time, and more robust car-following behavior. It also shows that the car-following thresholds are of the four combinations studies. The findings of this paper indicate that further research is required to develop a generalized car-following model that can be used when different vehicle types are in the traffic stream.

The operating speed profile is a useful tool for diagnosing potential safety issues on existing highways and for alignment design. Unfortunately, existing operating speed profile prediction models do not take into account the presence of intersections along roads because a procedure to estimate a reliable operating speed profile on the road section along which driver speed behavior is affected by an intersection is not currently available. To overcome this limitation, driver speed behavior approaching and departing from at-grade intersections and modern roundabouts was studied and a procedure to predict the operating speed profile along a road section characterized by the presence of an intersection was developed. This simple procedure uses the same construction rules as the existing operating speed profile models in order to be easily integrated into them. The procedure developed predicts a reliable profile in the proximity of at-grade intersections and roundabouts along two-lane rural roads, thereby improving significantly the possibility of applying this useful tool to safety evaluations.

In this paper, the common line problem and passengers’ choice behaviors in transit networks are revisited and discussed. The concept of the common line problem is extended to represent a common route problem in transit networks. The transit routes are classified into various levels of attractive route sets according to route fixed costs and route frequencies. The transit passenger assignment problem on congested transit networks is defined to assign passenger flows on attractive route sets and is formulated as a variational inequality (VI) problem. In the proposed model, the effective frequency approach is applied to reflect the effects of passenger congestion on the waiting time at the stations. Compared with traditional models, the proposed passenger flow assignment model does not require a modification to the transit network nor a constant recomputation of the changing attractive line set. Finally, a simple example is used to illustrate the difference between common line and common route methods, and the passengers’ travel behaviors under various transit conditions are demonstrated on the Sioux Falls transit network.

The pavement condition index (PCI) is a widely used numerical index for the evaluation of the structural integrity and operational condition of pavements. Estimation of the PCI is based on the results of a visual inspection in which the type, severity, and quantity of distresses are identified. The purpose of this study is to develop an alternative approach for forecasting the PCI using optimization techniques, including artificial neural networks (ANN) and genetic programming (GP). The proposed soft computing method can reliably estimate the PCI and can be used in a pavement management system (PMS) using simple and accessible spreadsheet softwares. A database composed of the PCI results of more than 1,250 km of highways in Iran was used to develop the models. The results showed that the ANN- and GP-based projected values are in good agreement with the field-measured data. In addition, the ANN-based model was more precise than the GP-based model. For more straightforward applications, a computer program was developed based on the results obtained.

Conventional pavements rely on stiff upper layers to spread traffic loads onto less rigid lower layers. In contrast, an inverted pavement system consists of an unbound aggregate base compacted on top of a stiff cement-treated base and covered by a relatively thin asphalt concrete layer. The unbound aggregate interlayer in an inverted pavement experiences high cyclic stresses that incite its inherently nonlinear granular media behavior. A physically sound, nonlinear elastoplastic material model is selected to capture the unbound granular base in a finite-element simulator developed to analyze the performance of inverted pavement structures. The simulation results show that an inverted pavement can deliver superior rutting resistance, as compared with a conventional flexible pavement structure with similar fatigue life.

This study quantifies and explains the paradox of a slow increase in road quality over time with a concomitant decrease in ride comfort or objective road quality. The study estimates the effect of road profile wavelength contents on the International Roughness Index (IRI) and planar personal half-car model vertical vibration response. Two cases are examined: (a) decrease in short-wave amplitudes ($<2\mathrm{m}$) and (b) increase in long-wave amplitudes ($>20\mathrm{m}$). The wavelength-content change was modulated by a change in the road profile spectrum slope (i.e., waviness) in both analyzed wavebands. The results indicate a relative insensitivity of the IRI algorithm to a change in road quality in both cases, while the particular vibration responses (dynamic tire force, seat or car body vertical acceleration) are markedly changed. It is shown that the IRI algorithm calculated for the alternative velocities should be a more robust tool to detect the change in wavelength contents in the examined wavebands.

Fatigue cracking is a major problem in asphalt pavement. It would be beneficial for predictions of the fatigue life of asphalt mixtures to be made with minimal effort and time, both at the design stage and the operating stage of pavement. For practicality, the indirect tension (IDT) test has widely been used for testing both laboratory-made and field-cored mixtures; however, a fatigue test using the IDT test needs to be performed in the stress-controlled mode rather than in the strain-controlled mode. Results may not be comparable to those from other mixture tests, such as the flexural beam fatigue or uniaxial tension test, so this study employed the viscoelastic continuum damage (VECD) model. The $C_1\text{-}{S}_1$ curve, which is a final product of the VECD model, is known as a single-characteristic curve of an asphalt mixture and is independent of loading types. Three $C_1\text{-}{S}_1$ curves obtained from two fatigue tests and one monotonic strength test using the IDT test were similar when superimposed into a single $C_1\text{-}{S}_1$ curve, indicating that the VECD model can be applicable both to the IDT fatigue test and the strength test. This also indicates that the fatigue life of a mixture subjected to sinusoidal strain loading can be determined through the model’s damage principles using the IDT test results. Consequently, a system for predicting the fatigue life of asphalt mixtures was developed. It is expected that the predictive system will be useful in estimating the fatigue life of asphalt mixtures fabricated in the laboratory and those cored from field pavements.

This paper discusses the effect of using different subballast solutions in the overall performance of the substructure on the basis of a methodology suitable to account for the effects of rail traffic loading and environmental actions in the hydro-thermo-mechanical performance of the subgrade over its service life. The mechanistic design of the different railway track configurations is first analyzed through a three-dimensional finite-element (FE) model, accounting for the adequate modeling of the sleeper-ballast interface. Then, a two-dimensional FE hydro-thermic analysis is developed to assess the effect of the different solutions in the performance of railway trackbed layers resulting from environmental effects (atmospheric actions and phreatic level variations) when modeling superficial drainage systems that enable the runoff of excess rainwater. Results from the mechanistic and hydro-thermic analysis were combined to predict the long-term deformational behavior of the subgrade. The proposed solutions with bituminous layers showed a better performance than the granular-only design concerning the substructure predicted service life cycle.

High-speed rail in infrastructure construction has become a priority worldwide, and vast investments have been made; therefore, it has become increasingly necessary to appropriately assess regional economic impact, and more importantly, to deliberate policy to improve regional industrial development. This paper looks at the impacts of Shinkansen lines on industrial locations in Japan. Unlike previous research, this study investigates both socioeconomic factors and physical determinants through an empirical industrial location model and multivariable stepwise regressions. The results indicate that during the period of 1990–2000, the dominant driving force of industrial location has changed from industrial transaction interdependence to population consumption demand. The elasticity of accessibility to the Shinkansen network has also shown an increasing trend from 1990 to 2000. Further expansion of Shinkansen lines would encourage the development of several industries, such as real estate, commerce, and services, and would thus contribute to forming the regional economic structure.

This paper investigates the impact of overlay type (asphalt and concrete) on the progression of pavement roughness as described by the international roughness index (IRI). Pavement roughness is defined as deformities or irregularities found at the pavement surface affecting the smoothness of a ride. Both overlays in this study are asphalt: one placed on asphalt paved roads and one placed on top of prior concrete pavement. Data were collected from nine states using data in the Long-Term Pavement Performance database (GPS6 and GPS7). Only U.S. states and Canadian provinces that have both GPS6 and GPS7 sections are considered for analysis. Pavement factors (overlay age, temperature, freezing index, and overlay thickness) that significantly impact overlay performance in U.S. and Canadian environments have been used for analysis. Multivariate regression analysis was used to compare the effect of key variables (age, temperature, freezing index, and surface thickness) on IRI. Results show that there is a significant difference between overlay types in terms of the effect of key variables on IRI. Asphalt on concrete overlay has a significantly better IRI performance in terms of age, temperature, and surface thickness. Asphalt on asphalt overlay has a significantly better performance in terms of freezing index. This suggests that when age, temperature, and surface thickness are considered, asphalt on concrete provides a better IRI. Asphalt on asphalt provides a better IRI for freezing index.