Seasonal demand variation is a somewhat neglected aspect of transit demand. Many cities worldwide experience seasonal variation of passenger demand that deserves not only changes of service frequency, but also and often more importantly, changes in the transit network of routes. Literature dealing with optimal bus networks shows that it is an non polynomial (NP)-hard complexity problem with the demand being the most important variable affecting the network. Thus, the bus network of concern should be robust from the optimization perspective when a new demand arises. This paper introduces a model and a solution algorithm to take into account the variation of passenger demand in the design of bus networks. The solution algorithm developed is a hybrid method that optimizes the design of a bus network at the route and network levels. The model was applied to the city of Mashhad in Iran with a population of over 3.2 million and 20 million visitors annually. The results are promising and demonstrate how to determine the best single network of bus routes to suit variable passenger demand.

Signalized intersections next to each other on the same arterial share some unobservable information, such as traffic flow and roadway characteristics. This study investigated the impact of access management techniques on crash counts at signalized intersections. The analysis was performed using crash data from 275 signalized intersections in southern Nevada. The panel data random-effect model was used to account for the unobserved factors for each unique arterial. It was found that the negative binomial (NB) regression models were the best in reflecting the dispersion in the crash data. Therefore, the random-effects negative binomial model (RENB) was applied to investigate the relationship between crash occurrence and access-management techniques. The results of the panel data RENB models were compared with those from the pooled NB models, which did not account for the panel data structure. Evaluation of the goodness-of-fit of the models developed indicated that the random-effect negative binomial model was the best-fit for the data at hand. The results from the panel data RENB showed that nine variables significantly affecting the safety at signalized intersections were the average length of corner clearance, traffic flow, land-use types, number of left-turn lanes for main streets, number of through lanes for main and minor streets, posted speed limit on main and minor streets, and grades of legs.

Historically, two-lane highways served primarily as uninterrupted flow connectors between major population centers. Increasingly, however, population growth in areas between the major population centers along these highways is reaching levels such that occasional signalized intersections are commonly present. Given increasing congestion along these routes, particularly as a result of increased signalization, transportation engineers need methods and tools to analyze highway performance. The current analysis methodology contained in the commonly used highway capacity manual (HCM) for two-lane highways is limited in scope to completely uninterrupted-flow facilities. Furthermore, the spacing of signalized intersections along a two-lane highway is typically great enough such that the urban streets analysis methodology of the HCM is not applicable. Until recently, a simulation tool did not exist that could model the combination of two-lane highway segments and signalized intersections. This capability is now incorporated in the corridor simulation (CORSIM) microscopic traffic simulation program. This study uses the updated version of CORSIM to develop an analytical methodology for two-lane highway facilities (i.e., the combination of two-lane highway segments and signalized intersections). This study is a follow-up to a similar one conducted in 2009 at the University of Florida. However, a major distinction is that the previous study had to use a hybrid simulation approach [TWO-lane PASsing and CORridor SIMulation (TWOPAS and CORSIM)] whereas this study used the improved version of CORSIM that provides a unified and more accurate simulation approach. Although many similarities exist in the results for this study and the preceding one, several differences in model structures and coefficients were identified in this study. The service measure percent delay was retained for its efficacy in assessing operations on complex two-lane highway facilities (particularly those with signalized intersections), but this paper presents new percent delay-based level-of-service criteria thresholds. This paper also provides additional guidance for facility segmentation in situations in which a speed limit reduction is applied in the vicinity of a signalized intersection.

This paper empirically investigates the multianticipative car-following behavior using a multiple linear regression approach. Two important aspects of the multiple linear regression of a modified generalized linear multianticipative car-following model are addressed, namely, multicollinearity between explanatory variables and the serial correlation of time series data, which have not been taken into account by previous studies. This paper analyzes the multianticipative car-following behavior using the next generation simulation (NGSIM) trajectory data. Specifically, the following issues are discussed: reaction time estimation, multicollinearity diagnosis, serial correlation test, model selection, and parameter estimation. Preliminary results show that a driver in extremely congested traffic conditions is able to react to the stimuli of the first, second, and even third leader with respect to the relative speed and the stimuli of the first or the second leader with respect to the desired space. It is reascertained that a driver is more perceptual to speed difference than to desired space behavior in extremely congested traffic conditions. Moreover, it is observed that a driver in less congested traffic conditions only reacts to the stimuli of the first leader with respect to both the relative speed and desired distance. Therefore, it is empirically believed that the stimuli perceived by a driver may be different in different traffic conditions with respect to multianticipative car-following behavior.

In the central business district (CBD) of metropolises, speed limits and movement prohibitions are often imposed on roads and intersections, respectively, for the purpose of safety and congestion mitigation and sometimes also to attempt to cut down vehicular aggregations and emissions. Based on this kind of general transportation network, this paper investigates the optimal time-based toll design problem for a cordon-based congestion pricing scheme in which toll charges are determined according to time consumed by travelers in traversing a predetermined cordon. Under the common hypothesis of travelers following the deterministic user equilibrium principle, this practical time-based toll design problem is formulated as a mathematical programming with equilibrium constraint (MPEC) model and solved by the Hooke-Jeeves algorithm, a derivative-free pattern search method. Taking into account the movement prohibitions at intersections, this paper presents a shortest-path model with movement prohibitions and develops an efficient branch and bound algorithm. Subsequently, the deterministic user equilibrium model can be solved by combining the proposed branch and bound algorithm with the method of successive averages (MSA). Finally, a medium-sized network example is adopted to numerically validate the proposed models and algorithms, and different features and results of four kinds of toll design schemes are further analyzed and discussed.

Dynamic message signs (DMSs) are a component of intelligent transportation systems (ITSs) used to convey real-time travel information to motorists, enabling them to make better decisions in response to real-time roadway conditions such as congestion, crashes, and adverse weather. The purpose of this paper is to analyze the existing DMS system implemented by the Wyoming Department of Transportation (WYDOT) along a rural segment of Interstate 80 in southeastern Wyoming between the cities of Laramie and Cheyenne. The research uses speed, weather, and DMS message data collected from the winter season of 2009 to 2010 along the corridor to determine how driver speed behavior is affected by DMS signs. A linear regression model utilizing four different weather severity categories was estimated and the results indicate that the DMS message signs are effective at reducing drivers’ speeds along rural interstate corridors from 8 km/h to 32 km/h (5 mi/h to 20 mi/h) above the speed reductions that can be accounted for by weather conditions alone.

Both patrolling and prepositioned strategies for allocating emergency traffic response units have been implemented in practice. To compare the performance of both response strategies, this study has conducted an efficiency comparison based on the field data from the I-495/I-95 Capital Beltway. The extensive experimental results have revealed that the effectiveness of those response strategies varies with some critical factors, including the spatial distribution of incident frequency over different times of a day, the fleet size of the response team, the congestion level, and the available detection sources. In view of the resource constraints, the study has further presented a methodology to determine the most cost-beneficial fleet size operated with the proposed strategies, considering the marginal cost and the benefit of an additional response unit on the resulting total social benefits. The analysis results with the data from the Capital Beltway could serve as the basis for highway agencies to review and optimize their incident response and management program.

Safety performance functions (SPFs) are commonly used to correlate geometric, traffic, and environmental characteristics with total crashes, and to identify hotspots that have excessive overall crash frequencies. However, different crash types are associated with different vehicle maneuvers and therefore different risk factors. At signalized intersections, geometric design, signal control, traffic flow, and traffic crash occurrences vary across different approaches of a single intersection. This study developed approach-level SPFs using a full Bayesian method to assess the safety effects of specific risk factors for rear-end, left-turn, right-angle, and sideswipe crash types, and for total crashes. Based on these approach-level SPFs, a systematic method that efficiently integrated the procedures of hotspot identification and countermeasure development was proposed. The method can be used to identify high-risk intersection approaches with specific safety problems and can serve as a useful complement to general hotspot analyses that use expected crash totals. It was found that some variables, including the number of through lanes, median presence, and left-turn protection, could have contrary effects on the occurrence of certain crash types. The proposed method can provide insights to aid in the development of countermeasures aimed at reducing certain crash types and an improved ability to identify deficiencies related to geometric and traffic characteristics for each intersection approach.

Pavement markings play an important role in the roadway system because they provide information for drivers to follow the road. Markings must be replaced when their retroreflectivity falls below an acceptable level. The rate of degradation of marking retroreflectivity can be influenced by many factors, such as type of material and traffic volume. The efficiency of pavement marking maintenance programs could be improved with statistical models that estimate the degradation of retroreflectivity over time. A case study based on data collected by the Alabama DOT was undertaken to address these issues. The purpose of the research reported in this paper was to observe how existing models fit the data and develop new statistical models that could predict retroreflectivity over time for Alabama conditions. Modeling of retroreflectivity performance over time for thermoplastic markings was executed for 15 projects that had measurements of retroreflectivity for the same locations in years 2007–2010. The modeling of retroreflectivity considered initial retroreflectivity, age, and annual average daily traffic as candidate independent variables. A linear model well-represented the data in Alabama and variable selection methods showed that initial retroreflectivity is not significant for retroreflectivity estimation. The models developed in the research reported in this paper perform better than most of the models found in the literature as shown by a comparison of correlation coefficient values. The linear model based on age and traffic volume, with correlation coefficient values of 0.448 and 0.488 for white and yellow markings, respectively, is recommended for prediction of future retroreflectivity performance and scheduling of maintenance activities.