•We develop a model of departure time and parking location choices by heterogeneous commuters.•We examine the distributional effects of imposing a congestion toll and/or a parking fee.•The expansion ...of parking capacity does not necessarily lead to a Pareto improvement when a parking fee is not imposed.•The self-financing principle holds separately for bottleneck capacity and parking capacity.
This study examines the effects of a time-varying congestion toll and a location-dependent parking fee on the behavior of heterogeneous commuters and their commuting costs. To this end, we develop a model of departure time and parking location choices by heterogeneous commuters and characterize its equilibrium. By comparing the equilibrium with and without pricing policies, we obtain the following results: (1) imposing a parking fee and expanding parking capacity may concentrate the temporal distribution of traffic demand, thereby exacerbating traffic congestion; (2) the expansion of parking capacity does not necessarily lead to a Pareto improvement when a parking fee is not imposed; (3) the social optimum is achieved by combining a parking fee with a congestion toll; and (4) the revenue obtained from pricing of roads and parking exactly equals the costs for optimal bottleneck and parking capacities, respectively; that is, the self-financing principle holds separately for bottleneck capacity and parking capacity.
•Probe data reveals traffic condition immediately after earthquake and tsunami.•It indicates a sudden transition of the vehicle speed lower than walking speed.•The estimated traffic condition reveals ...the serious gridlock in Ishinomaki.
This study analyzes how people behaved and traffic congestion expanded immediately after the Great East Japan Earthquake on March 11, 2011 using information such as probe vehicle and smartphone GPS data. One of the cities most seriously damaged during the earthquake was Ishinomaki. Understanding human evacuation behavior and observing road network conditions are key for the creation of effective evacuation support plans and operations. In many cases, however, a major natural disaster destroys most infrastructure sensors and detailed dynamic information on people’s movements cannot be recorded. Following the Great East Japan Earthquake, vehicle detectors did not work due to the severe tsunami and electric power failure. Therefore, information was only available from individuals’ probe vehicles and smartphone GPS data. These probe data, along with disaster measurements such as water immersion levels, revealed the sudden transition of vehicle speed (i.e., it eventually slowed to less than walking speed and a serious gridlock phenomenon in the Ishinomaki central area occurred). These quantitative findings, which could not be identified without probe data, should be utilized during future disaster mitigation planning.
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•The network-wide traffic state spatial interpolation by a mixture GGM.•The mixture GGM reasonably estimated the unobserved road link speeds.•It is useful for traffic state ...interpolation and understanding traffic state change.
This study proposes a model that estimates unobserved highway link speeds by a machine learning technique using historical probe vehicle data. For highway traffic monitoring, probe vehicle data is one of the most promising data source. However, since such data do not always cover an entire study area, we cannot measure traffic speeds on all links in a time-dependent manner; quite a few links are unobserved. To continuously monitor speeds on all links, it is necessary to develop a technique that estimates speeds on unobserved links from historical observed link speeds. For this purpose, we extend the current Gaussian graphical model so as to use two or more multivariate normal distributions to accurately estimate unobserved link speeds. In general, since the number of unknown model parameters (mean parameters and covariance matrices) is enormous and also unobserved links always exist, the EM algorithm and the graphical lasso technique are employed to determine the model parameters. Our proposed model was applied to the Bangkok city center in Thailand as well as to the Fujisawa city in Japan. We confirmed that the model can estimate the unobserved link speeds quite reasonably.
•A state-space model estimating traffic states on a 2-dimensional network is proposed.•SSM consists of CTM and probe vehicle measurements of densities and diverging ratios.•Particle filtering is used ...to estimate the traffic states as well as model parameters.•The model validation is made on a hypothetical network.
This study proposes a state-space model that estimates traffic states over a two-dimensional network with alternative routes available by a data assimilation technique that fuses probe vehicle data with a traffic flow model. Although a number of studies propose traffic monitoring methods based on physical flow dynamics using sensing data such as probe vehicle and traffic detector data, they are basically limited to traffic monitoring along a simple road section. This study extends the analysis to a two-dimensional network, in which several alternative routes exist for each OD, with consideration of the route choice behaviours of users. Our proposed method employs sequential Bayesian filtering with a cell transmission model (CTM) for the flow model and probe vehicle data. From the probe vehicle data, not only cell densities but also diverging ratios are assumed to be measured and these measurements are assimilated into the flow model. The model validation in a hypothetical network reveals the potential of the model and discloses future issues.
•This study proposed a method using measurements from a vehicle on the opposite lane.•This method has the advantage in quickly responding unexpected incidences.•We could estimate the time period of ...an accident and the capacity value.
This study proposes a method that estimates traffic states using measurements from a vehicle running on the opposite lane in addition to probe vehicle data and examine the sensitivity of the estimates in relation to variabilities of the input data and measurements. A number of studies on traffic state estimation fusing several sensing data have been reported. Most of the studies use data from traffic detectors installed at fixed locations and data from moving objects such as probe vehicles. Traffic detectors provide valuable volume information of all running vehicles which cannot be observed from sample moving objects. However, in local areas in Japan as well as in Asian cities, detector installations are very much limited like one in every 10 to 15 km on a motorway in our country. This study therefore attempts to utilize measurements from a vehicle running on the opposite lane instead of detector measurements, since a vehicle on the opposite lane running backward can in principle measure counts of passing vehicles running forward. In this study, we employ the variational theory to estimate the traffic states utilizing the count measurement from the opposite lane in addition to probe vehicle data on the forward direction and examine the sensitivity of the estimates in relation to variabilities of the input data and measurements. The validation finds that the proposed method can estimate traffic states more accurately than one using only probe vehicle data. Especially, this method has the advantage in quickly responding unexpected incidences such as accidents and vehicle malfunctions.
This paper extends the conventional static marginal cost analysis to the dynamic one based on the time-dependent queueing analysis at a bottleneck. First, the supply function is reformulated so as to ...incorporate dynamically congestion phenomena. And, the marginal cost is shown to be more closely related to the duration of congestion rather than the personal cost, since a slight change of demand at one time affects an entire traffic condition thereafter. Next, the analysis is extended so as to include the departure time choice using previous departure time choice theory. Several implications such as road pricing schemes and ramp control strategies are also discussed.
•Consider coordinated traffic signal controls under both deterministic and stochastic demands.•A new mixed integer linear programming (MILP) for deterministic optimization is proposed.•The MILP has a ...network structure and its size is smaller than the existing formulations.•The problem is extended to treat the stochastic fluctuations in traffic demand.•An accurate and efficient approximation method of expected delays is developed.•Optimal control parameters for deterministic and stochastic controls are examined.
This study considers an optimal coordinated traffic signal control under both deterministic and stochastic demands. We first present a new mixed integer linear programming (MILP) for the deterministic signal optimization wherein traffic flow is modeled based on the variational theory and the constraints on a signal control pattern are linearly formulated. The resulting MILP has a clear network structure and requires fewer binary variables and constraints as compared with those in the existing formulations. We then extend the problem so as to treat the stochastic fluctuations in traffic demand. We here develop an accurate and efficient approximation method of expected delays and a solution method for the stochastic version of the signal optimization by exploiting the network structure of the problem. Using a set of proposed methods, we finally examine the optimal control parameters for deterministic and stochastic coordinated signal controls and discuss their characteristics.
► A methodology is proposed to combine fixed and probe sensor data to reconstruct vehicle trajectories on signalized arterials. ► Probe trajectories are used as reference to reconstruct the ...trajectories of other vehicles. ► Proposed method is applied to real world data and its robustness is confirmed by changing the input data characteristics. ► The basic methodology is extended so as to incorporate the vehicles coming in and out from midblock intersections.
A data fusion framework is examined to reproduce vehicle trajectories on urban arterials by combining probe and fixed sensor data, and signal timing parameters. The methodology is based on the kinematic wave theory and employs the variational theory for the solution method. However, the original methodology cannot deal with the vehicles coming in and out in the middle of the study section despite the frequent existence of such vehicles in the real world. Therefore, the methodology is extended so as to incorporate the vehicles coming in and out. The proposed method is then applied to real world data and its robustness is confirmed by changing the input data characteristics.
In this study, the traffic state of a commercial vehicle was analyzed from a macroscopic viewpoint by using the probe data of a commercial vehicle in the Shikoku region during a period of heavy rain ...that occurred in western Japan in July, 2018. A method is proposed to calculate indexes, such as the detour rate and reduction in the number of trips, through an analysis of a trip at each origin-destination (OD) and extracting the route of a detouring vehicle during a disaster by using the results of the calculation. Finally, a method for the early detection of abnormalities, which involves paying attention to U-turn action during traffic disturbances is proposed. The influence of heavy rain on a commercial vehicle was evaluated quantitatively by analyzing the probe data of the vehicle during a disaster period caused by heavy rain. Specifically, analysis was performed on the number of passing commercial vehicles before and after the occurrence of a disaster, changes in running speed, route changes at each OD, and the vehicle trajectory around a regulated area. From the results of the analysis, it was possible to grasp the macroscopic traffic state, OD influenced by the traffic restriction, route in use for the OD during a normal time period, and an alternate route (detour action) during the disaster time period. With the method for the early detection of abnormalities at the time of a traffic disturbance, which pays close attention to U-turn action, a U-turn after the traffic regulation can be detected; however, it was confirmed that there is a problem in detecting timing and the application range.
In this study, we detect the detours of commercial vehicles during heavy rains in western Japan using machine learning technology and then analyze the cause of these detours. Due to heavy rains in ...2018 in western Japan, road regulation was implemented over a wide area. GPS-generated probe trajectories revealed the detour routes taken. The necessity of taking detours is one of the traffic failures caused by disasters. To identify these detours, a road administrator must visually check and analyze the probe vehicle trajectory, which requires considerable labor. Therefore, in this study, we detected detours during a disaster by learning the probe vehicle trajectory under normal circumstances using a one-class support vector machine (OCSVM). Results of detour detection for Shikoku revealed that vehicles were using distant detour routes even when nearer detour routes were accessible. An analysis of the cause of these detours showed that the “risk” of the traffic failure was one factor.
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