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  • Mobility and traffic research  (7)
  • 1
    Online Resource
    Online Resource
    Use of Support Vector Machine Models for Real-Time Prediction of Crash Risk on Urban Expressways
    SAGE Publications ; 2014
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2432, No. 1 ( 2014-01), p. 91-98
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2432, No. 1 ( 2014-01), p. 91-98
    Abstract: This study adopted a novel methodology—a support vector machine (SVM) with two penalty parameters—for the evaluation of real-time crash risk on urban expressway segments by using dual-loop detector data. The purpose of this study was to develop a model that can effectively identify traffic conditions prone to crashes and support implementation of proactive traffic safety management. On the basis of crash data and the corresponding detector data collected on expressways of Shanghai, China, different combinations of dual-loop detector data and time segments before crashes were used to develop the optimal crash risk estimation model by SVM. The transferability of the SVM model was assessed by examining whether the model developed on one expressway was applicable to other similar ones. In addition, the prediction results and transferability of the SVM model were compared with those given by other frequently used classification algorithms, including logistic regression, Bayesian networks, naïve Bayes classifier, k-nearest neighbor, and back propagation neural network. The results showed that the SVM model was more suitable to the prediction of real-time crash risk with small-scale data than other algorithms, with its accuracy in classifying crashes reaching a best of 80%. A comparison to similar studies by other researchers implied that the proposed model achieved better prediction accuracy.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2432-11
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2014
    detail.hit.zdb_id: 2403378-9
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  • 2
    Online Resource
    Online Resource
    Stability Evolution of Car-Following Models Considering Asymmetric Driving Behavior
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 8 ( 2023-08), p. 361-371
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 8 ( 2023-08), p. 361-371
    Abstract: In this paper, we relax an unrealistic assumption that is commonly adopted in the stability analysis of car-following (CF) models, that is, the equilibrium state is fixed. Specifically, the influence mechanism of the equilibrium state and equilibrium state change on the stability of CF models is studied considering the impact of asymmetry in CF models. Two state change processes are considered: acceleration and deceleration processes for symmetric and asymmetric CF models. The results reveal that significant differences exist between the two types of CF models: while the acceleration process would significantly destabilize the traffic with the asymmetric CF model, the influence of acceleration and deceleration on its stability change is identical and relatively unsignificant for the symmetric CF model.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981231156584
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 3
    Online Resource
    Online Resource
    Car-Following Characteristics of Commercially Available Adaptive Cruise Control Systems and Comparison With Human Drivers
    SAGE Publications ; 2023
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2677, No. 2 ( 2023-02), p. 1401-1414
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2677, No. 2 ( 2023-02), p. 1401-1414
    Abstract: Adaptive cruise control (ACC) system, as one of the most fundamental modules of automated vehicles, is widely used in commercially available vehicles. It inevitably influences the traffic flow, from both the individual perspective, that is, its interaction with other traffic participants, and the traffic system perspective, that is, traffic string stability and road capacity. However, subject to limited data availability, no consistent conclusions on these impacts have been reached in the literature. Meanwhile, the similarities and differences between ACC vehicles and human-driven vehicles (HDV) have not been fully discussed and comparisons among different commercially available ACC systems remain to be untangled. Therefore, to fill this gap, this study investigates the car-following characteristics of various ACC systems and compares them with human drivers based on the open-access OpenACC database. We first identify the proper surrogate car-following model for denoting the driving behaviors of ACC vehicles and HDVs from five widely used car-following models, among which the best-fitted one is the intelligent driver model. Then, we implement the Gaussian mixture model and Jensen-Shannon divergence to describe the similarities between ACC systems and human drivers. Moreover, the string stability of different ACC platoons in various traffic conditions are investigated with a series of simulation experiments. Results show that all the ACC systems are string unstable, and more unstable than human drivers. The behavior of the Ford-ACC system is most similar to human drivers with a relative low instability, while the Peugeot-ACC system behaves most differently to human drivers, and aggressively with the highest instability.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/03611981221113313
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
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  • 4
    Online Resource
    Online Resource
    Risk Prediction for Curve Speed Warning by Considering Human, Vehicle, and Road Factors
    SAGE Publications ; 2016
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2581, No. 1 ( 2016-01), p. 18-26
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2581, No. 1 ( 2016-01), p. 18-26
    Abstract: Current curve speed warning systems take into account mostly vehicle and road factors but not driver behavior. The systems aim at detecting sideslips of small cars on curves without consideration of rollovers for vehicles with an elevated center of gravity. In this study, a curve speed model that considers human, vehicle, and road factors is built to prevent not only sideslips but also rollover accidents for vehicles with an elevated center of gravity. In addition, a risk prediction model is presented to judge accident risk levels and determine levels of warning. Finally, the effectiveness of the presented system is validated with one skilled driver who carries out one test through a simulator under different curve scenarios. To verify the system, data from simulator tests were collected for offline checking of the system. The data were used to calculate safe speeds by using the curve speed model and to determine the levels of risk based on the risk prediction model. The results show that the system is highly compatible with the skilled driver in terms of warning accuracy and timing. Specifically, the correct alarm rate (i.e., the driver brakes and the system’s alarm goes off) of the system is 83.57% and the error alarm rate (i.e., the driver does not brake but the system’s alarm goes off) is 9.79%. Moreover, more than 80% of the time the difference between the system warning time and the operating time of the skilled driver is less than 2 s.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2581-03
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
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  • 5
    Online Resource
    Online Resource
    Clustering of Several Typical Behavioral Characteristics of Commercial Vehicle Drivers Based on GPS Data Mining: Case Study of Highways in China
    SAGE Publications ; 2016
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2581, No. 1 ( 2016-01), p. 154-163
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2581, No. 1 ( 2016-01), p. 154-163
    Abstract: Some studies of driving behavior have been based on data mining to create a mechanism that relates data derived from vehicle monitoring, driver behavioral characteristics, and road safety to each other. To make the best of GPS data collected by transportation businesses and explore the potential rules of commercial vehicle driver behavioral characteristics, the parameters related to driving behavioral characteristics are extracted according to GPS data attributes based on factor analysis, and eight parameters of driving behavioral characteristics are transformed into a few aggregated variables containing clear information about driving behavior. With these variables as indicators, a cluster analysis of commercial vehicle driver behavioral characteristics in the selected case is carried out through hierarchical clustering. The results show that commercial vehicle driver behavioral characteristics can be effectively aggregated into four kinds: acceleration–deceleration, speeding-prone, acceleration, and deceleration. Of the four kinds, drivers with relatively serious acceleration–deceleration behavior are also characterized by three other relatively serious behaviors; such drivers have relatively high driving risks, so transportation businesses need to focus their supervision on those drivers. The research results have some relevance to the supervision and training of commercial vehicle drivers in China.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2581-18
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
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  • 6
    Online Resource
    Online Resource
    Development of Multiregime Speed–Density Relationships by Cluster Analysis
    SAGE Publications ; 2005
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 1934, No. 1 ( 2005-01), p. 64-71
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 1934, No. 1 ( 2005-01), p. 64-71
    Abstract: Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198105193400107
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2005
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  • 7
    Online Resource
    Online Resource
    Determination of Load Equivalency for Unpaved Roads
    SAGE Publications ; 2015
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2473, No. 1 ( 2015-01), p. 233-241
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2473, No. 1 ( 2015-01), p. 233-241
    Abstract: The load equivalency method is widely used to consider the effect of traffic loading on pavement design, and the equivalent axle load factor (EALF) for paved roads has been studied often. For unpaved roads, however, EALF is not well understood because it is not necessarily the same as it is for paved roads. In this study, cyclic plate load tests were conducted on unpaved road sections (six base-over-subgrade sections and four subgrade-only sections) constructed in a geotechnical box (2 m × 2.2 m × 2 m) to investigate the load equivalency for unpaved roads. The base-over-subgrade sections were constructed as unstabilized, T1 geogrid–stabilized, and T2 geogrid–stabilized base courses of 15% California bearing ratio (CBR) with thicknesses of 0.23 m and 0.30 m over weak subgrade of 2% CBR. The subgrade-only sections were constructed with CBR values of 6.2%, 7.4%, 9.5%, and 11.0%. The intensities of the cyclic loads were increased from 5 kN to 65 kN, at increments of 5 kN. For each load intensity, 100 cycles were applied on one test section. The EALFs were analyzed in terms of permanent deformation. The results showed that the regression powers of the ratios of axle loads for unpaved roads with aggregate bases over weak subgrade ranged from 1.9 to 2.9, which were lower than a power of 4, the typical value used for paved roads. The powers for subgrade-only sections had an even wider range, from 1.1 to 3.4. The increase of base thickness, the presence of geogrid, and the use of a higher-grade geogrid increased the power.
    Type of Medium: Online Resource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.3141/2473-27
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
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