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This paper proposes a multi-criteria user equilibrium model considering travel time, travel time reliability and distance (MUE-TRD). This new model hypothesizes that for each user class and each origin-destination (O-D) pair no traveler can reduce either his or her reliable travel time or travel distance or both without worsening the other objective by unilaterally changing routes in their route choice decision process. Travel time budget which consists of travel time and travel time reliability is used to describe the reliable travel time. A maximum entropy multi-criteria user equilibrium (ME-MUE) model is presented to address the non-uniqueness of the solution in MUE-TRD model. Furthermore, a route-based solution algorithm based on the partial linearization descent method (R-PLD) is developed to solve the ME-MUE model. Numerical examples are also provided to illustrate the essential ideas of the proposed model and the applicability of the developed solution algorithm. The results show that compared to traditional user equilibrium and travel time budget models, ME-MUE model is more consistent with the real trip process that the reliable travel time is increasing with the decreasing of travel distance in used routes; and the road traffic is smoother when using ME-MUE model to design the road network, thus ME-MUE model can reduce road traffic noise and air pollution in the urban road network.

Since traffic origin-destination (OD) demand is a fundamental input parameter of urban road network planning and traffic management, multisource data are adopted to study methods of integrated sensor deployment and traffic demand estimation. A sensor deployment model is built to determine the optimal quantity and locations of sensors based on the principle of maximum link and route flow coverage information. Minimum variance weighted average technology is used to fuse the observed multisource data from the deployed sensors. Then, the bilevel maximum likelihood traffic demand estimation model is presented, where the upper-level model uses the method of maximum likelihood to estimate the traffic demand, and the lower-level model adopts the stochastic user equilibrium (SUE) to derive the route choice proportion. The sequential identification of sensors and iterative algorithms are designed to solve the sensor deployment and maximum likelihood traffic demand estimation models, respectively. Numerical examples demonstrate that the proposed sensor deployment model can be used to determine the optimal scheme of refitting sensors. The values estimated by the multisource data fusion-based traffic demand estimation model are close to the real traffic demands, and the iterative algorithm can achieve an accuracy of 10−3 in 20 s. This research has significantly promoted the effects of applying multisource data to traffic demand estimation problems.

This paper extends the work on Pareto-improving hybrid rationing and pricing policy for general road networks by considering heterogeneous users with different values of time. Mathematical programming models are proposed to find a multiclass Pareto-improving pure road space rationing scheme (MPI-PR) and multiclass hybrid rationing and pricing schemes (MHPI and MHPI-S). A numerical example with a multimodal network is provided for comparing both the efficiency and equity of the three proposed policies. We discover that MHPI-S can achieve the largest reduction in total system delay, MHPI can induce the least spatial inequity and MHPI-S is a progressive policy which is appealing to policy makers. Furthermore, numerical results reveal that different classes of users react differently to the same hybrid policies and multiclass Pareto-improving hybrid schemes yield less delay reduction when compared to their single-class counterparts.

With the development of the advanced Intelligent Transportation System (ITS) in modern cities, it is of great significance to upgrade the forecasting methods for travel demand with the impact of ITS. The widespread use of ITS clearly changes the urban travelers&rsquo, behavior at present, in which case it is difficult for the conventional four-step travel demand forecasting model to have good performance. In this study, we apply the combined distribution and assignment (CDA) model to forecasting travel demand for modern urban transportation, in which travelers may choose the destination and path simultaneously. Furthermore, we present a new solution algorithm for solving the CDA model. With the network representation method that converts the CDA model into a standard traffic assignment problem (TAP), we develop a new path-based algorithm based on the gradient projection (GP) algorithm to solve the converted CDA model. The new solution algorithm is designed to find a more accurate solution compared with the widely used algorithm, the Evans&rsquo, two-stage algorithm. Two road networks, Sioux Falls and Chicago Sketch, are used to verify the performance of the new algorithm. Also, we conduct some experiments on the Sioux Falls network to illustrate several applications of the CDA model in consideration of the influences of ITS.

This paper addresses the network sensor location problem (NSLP) for identifying the set of sensor locations that minimizes the variability in estimation of traffic flow given budget constraints. The trace of the covariance matrix is adopted as a measure of variability in traffic flow. On the basis of the trace of the covariance matrix in the posterior estimation of traffic flow conditional on a given set of sensor locations, the general form of the NSLP is derived. As an illustration, the multivariate normal distribution for the prior estimation of traffic flow is assumed. In this case, the actual value of the counted flows is not required. Furthermore, an incremental method that can avoid matrix inversion and give priorities of the identified sensor locations is presented to solve the NSLP. Finally, a numerical example based on the Nguyen–Dupuis network illustrates the NSLP approach and clarifies some of its implementation details.

The intersection is an area where many accidents occur. Reasons for accidents are due to complicated intersection designs and the congested travel conditions. For these reasons, traffic incident detection is more complicated. This paper uses the intersections of Huaihai South Road and Jiefang Road as an example. According to the vehicle operation and phase timing, the situations of two vehicle's relative movement on four phase intersections are summarized. The motion vectors of the conflicting vehicle are quantized on the basis of vehicle tracking. Then, the HMM is used to classify the traffic conflicts of the intersection. Finally, numerical experiments verify that the algorithm is able to classify the conflict when the traffic is normal. Furthermore, the algorithm can forecast the traffic accidents (such as bumping, tandem, and stop) which occurred in the intersection.

The aim of this study is to assess the long-term clinical and radiological outcomes between minimally invasive (MIS) and conventional transforaminal lumbar interbody fusion (TLIF) in treating one-segment lumbar disc herniation (LDH).One-hundred and six patients treated by MIS-TLIF (50 cases) or conventional TLIF (56 cases) were included. Perioperative results were evaluated. Clinical outcomes were compared preoperatively and postoperatively. Radiologic parameters were based on a comparison of preoperative and three-year postoperative lumbar lordosis, segmental lordosis, sacral slope, the cross-sectional area of the paraspinal muscle and fusion rates.MIS TILF had significantly less blood, shorter operation time, mean return to work time and lower intramuscular pressure compared with the conventional group during the operation. VAS scores for lower back pain and ODI in MIS-TLIF were significantly decreased. The mean cross-sectional area of the paraspinal muscle was significantly decreased after surgery in the conventional TLIF group and no significant intragroup differences were established in the MIS-TLIF group. No significant differences were found in fusion rate, lumbar lordosis, segmental lordosis and sacral slope.Both MIS and conventional TLIF were beneficial for patients with LDH. However, MIS-TLIF manifests a great improvement in perioperative outcomes, low back pain, disability and preventing paraspinal muscle atrophy during the follow-up period observation.

Recently, Lawphongpanich and Yin (2010) proposed a Manifold Suboptimization algorithm for finding a “strongly stationary” solution to a Pareto-improving toll problem by solving a sequence of relaxed problems. This note shows that Lawphongpanich and Yin’s algorithm may not always converges to satisfactory solutions. In this paper, the authors provide several examples to demonstrate that the manifold suboptimization algorithm is inefficient in solving mathematical programs with complementarity constraints (MPCC) under certain conditions. This note also offers some insights and directions to improve the manifold suboptimization problems in the application of solving bi-level programs.

Tradable mobility credit (TMC) schemes are proposed as an innovative way of managing network mobility by Yang and Wang since 2011. In the past three years, we have witnessed the development of a vast, growing body of research focused on formulation and solution procedures for tradab le credit schemes. In this paper, we present a comprehensive review on a variety of tradable credit schemes. First, we explore the development of tradable credit schemes and summarize the characteristics of tradable credits schemes with respect to qualitative properties, mathematical models and theoretical properties. Then, we investigate the variants of tradable credit schemes from 12 perspectives. Finally, we make concluding remarks and offer some perspectives for future research.

Despite the success of congestion pricing projects worldwide and the growing support from governments and transportation agencies, congestion pricing is still facing strong objection among the general public based on the worries that congestion pricing is inequitable. This paper offers an overview of the literature on the equity issues of congestion pricing. First, different definitions and measures of equity are illustrated. Then, equity effects of congestion pricing are presented. Finally, the approaches to promote the equity of congestion pricing are discussed.

Knowledge of departure times of commuter trips plays an important role, providing a basis for future policy making and for forecasting the peak hour without a great deal of investigation. This paper has proposed a departure time choice model based on prospect theory, considering that travelers are not entirely rational, being affected by the environment, the ability to obtain traffic information, and so on. In the case study, travelers are divided into several groups by their risk attitude (risk averse, risk neutral, risk appetite). We then find the difference of in the departure time amongst different the kinds of travelers, providing evidence for road network construction and traffic management.

This study aimed to investigate the expression of brain-derived neurotrophic factor (BDNF) in dorsal root ganglia (DRG) of a rat model of lumbar spinal stenosis (LSS). Adult male rats were divided into the operation and sham operation groups. The operation group was comprised of the rat models of LSS. Walking distance and BDNF expression levels in DRG were measured in the two groups at different time points. The total BDNF protein levels and positive cell mean optical density (MOD) values in the operation group were significantly higher at each time point compared with that of the sham operation and preoperative control groups (P0.05). The total BDNF protein levels and MOD values following sport in the operation group were significantly higher compared with those prior to sport (P0.05). In the sham operation group, BDNF protein levels and MOD values before and after sport at each time point showed no significant differences than those of the operation group (P0.05). Moreover, BDNF protein levels and MOD values in the operation group indicated a negative correlation with walking distance. The present study demonstrated that the expression of BDNF in rat models of LSS increased with time and was associated with a decrease in walking distance. BDNF was therefore important for the process of intermittent claudication caused by LSS.

Activity-based models belong to the third generation of travel demand models, which have received extensive interest in the past three decades. Activitybased modeling of travel demand treats travel as being derived from the demand for activity participation. This paper presents an overview of recent and on-going contributions made by “activity-based approaches” to forecast travel behavior. First, a brief history of two travel demand approaches is introduced. Then, four modeling methods in activity travel analysis are reviewed. On top of that, essential applications of this approach for addressing contemporary policy and planning issues are provided. Finally, some of the directions are highlighted for future development of activity-based analysis.

This paper mainly focuses on the characteristics of urban low-income residents' travel activities. Data is obtained from a residents' travel survey of Nanjing City (2009) and a specific travel survey of low-income residents in the urban fringe of Nanjing City (2010). Information of each individual's activity participation and trip chains is extracted from the daily travel diary and matched with attributes of the individual and family. Then, two structural equation models are applied to analyze the relationship among individuals' socio-demographics, activity duration, and trip chains of low-income residents and non-low-income residents. 13 socio-demographics attributes are chosen as the exogenous variables, while 8 indices of activity duration and trip chains are selected as the endogenous variables. The result shows that the number of children, age, gender, and educational level of an individual significantly affect the travel behavior of low-income residents. Also, activity participation has greater effects on trip-chaining behaviors of low-income residents than that of non-low-income residents.

The aim of this study was to investigate the biomechanical mechanisms of treatment of thoracolumbar compression fracture with pedicle screws at injury level based on a three-dimensional finite element method. We constructed one three-dimensional finite element model of T11-L1 in a patient with a compression fracture of the T12 vertebral body(anterior edges of vertebral body were compressed to 1/2, and kyphosis Cobb angle was 18.6°) fixed by four pedicle screws and another model fixed by six pedicle screws at the injured vertebrae, and then assigned different forces to the two models to account for axial compression, flexion, extension, left lateral bending, and rightward axial rotation by Ansys software. After different loading forces were applied to the models, we recorded stress measurements on the vertebral pedicle screws, as well as the maximum displacement of T11. The stress distribution suggested that stress concentration was appreciable at the root of the pedicle screws under different loading modalities. Under axial compression, flexion, extension, left lateral bending, and rightward axial rotation load, the stress for the superior screw was significantly greater than the stress for the inferior screw (P < 0.05). The stress in the six pedicle screw fixation model was significantly decreased compared to the four screw interbody fusion model (P < 0.05), but the maximum displacement of T11 between two models under different loadings was not statistically different. The use of pedicle screws at injured vertebral bodies may optimize internal fixation load and reduce the incidence of broken screws.