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As the electric vehicle industry continues to grow on a large scale, the challenges related to the locating and sizing of charging stations are becoming more apparent. This paper utilizes floating car data from Urumqi, considering factors such as battery SoC, usage time, and the diverse preferences of electric vehicle drivers when making charging decisions. Through this approach, this paper predicts the charging demands of electric vehicles and formulates a model for optimal locating and sizing based on these predicted needs. The research findings indicate that by simulating charging decisions made by electric vehicle drivers, a total of 10,766 charging demands were identified. In comparison, there were 14,759 parking events extracted from the floating car data. This approach reduced redundancy in charging demand predictions compared to directly using the floating car data for extracting parking events. When considering the locating of charging stations, the strategy of exclusively installing fast-charging stations showed better results than the approach involving a mix of fast and slow charging stations. When the decision of locating prioritized minimizing unmet charging demands, it successfully addressed a minimum of 130 additional charging demands. Similarly, when minimizing the idle time for electric taxi drivers was the primary concern, this approach led to a reduction of at least 109 hours in idle time; through a reallocating and sizing adjustment of charging piles within the charging station based on the locating plan, it was possible to meet an additional 83 charging demands. This modification also significantly reduced the average idle time for users, decreasing from an average of 4.26 minutes to 2.63 minutes. Moreover, the charging piles’ average usage time increased, going up from 10.31 hours to 10.42 hours, thus improving the overall efficiency of the charging piles. These findings provide valuable insights for the locating and sizing of charging stations tailored to urban electric taxis.

Travel time to a destination is a key piece of information that motorists desire. Traditionally, transportation agencies collect traffic state information with costly measurement sensors. In the last decade, private sector data, as an emerging data source, has been gradually adopted by many public agencies for performance measurement and travel time provision, mostly on freeways. Agencies have also used data from infrastructure sensors and floating cars to validate the quality of the private sector data on both freeways and arterials under different conditions. Although freeway data quality has been validated by many studies, nonfreeway travel time data have been considered unreliable, particularly when the segment is congested and high-density signals exist. In recent years, data vendors started to provide high-resolution travel time data, which are provided on much shorter segments than conventional traffic message channels (TMC). This study investigates the data quality of this new data source for providing travel times on nonfreeways. It evaluates the selected private sector data on nonfreeways by using floating car and Wi-Fi travel time data as the ground truth. The results show that the new private sector data on nonfreeways are generally acceptable for segments with low and moderate congestion. With higher congestion, the data quality is site-dependent. A neural network model is proposed to implicitly identify the error of a site and provide enhanced travel times. It is recommended that these new data should be validated for the deployment sites due to the site-dependent errors, and advanced data modeling techniques can be used to compensate for such errors. [ABSTRACT FROM AUTHOR]

The development of geospatial big data makes it possible to study traffic-congestion issues. In particular, floating car data (FCD) is very suitable for it because FCD can help predict traffic-congestion bottlenecks and provide corresponding solutions to address traffic problems. Previous studies have discussed the impacts of rainfall on road speeds, but few studies have focused on the impacts of rainfall on the spatial distribution and changes in traffic-congestion bottlenecks throughout a mega-city. This article proposes an index calculation and clustering (ICC) model by integrating PageRank and clustering algorithms from multisource data, including rainfall data, FCD, and OpenStreetMap data. As the study area, we selected Shenzhen, which is the largest developed city in South China. The results demonstrate three peak periods of citizen travel, namely, 8:00-10:00, 14:00-16:00, and 18:00-20:00. Road speeds after rainfall decrease by 6.20% on weekdays and by 2.37% on weekends, and traffic-congestion areas increase by 23.53% and 20.65% on weekdays and on weekends, respectively. In addition, rainfall causes more significant effects on traffic conditions on weekdays compared with on weekends in Shenzhen. Compared with a traditional kernel density analysis, the proposed ICC model can offer a more thorough understanding of urban traffic-congestion areas, which can help policy makers optimize alleviation strategies.

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Travel time is an important performance measure used to assess traffic operational quality of various types of facilities. Previous efforts to estimate travel time have also compared model-estimated travel times to field-measured travel times using various sources of data. Given the variety and diversity of travel time measurement methods, it is important to evaluate the accuracy of the data obtained by each of them and to develop recommendations regarding their suitability in the validation of travel time estimation models. The research objective for this paper was to collect field data along several freeways and arterials and to evaluate the travel times obtained by STEWARD (Statewide Traffic Engineering Warehouse for Regionally Archived Data), INRIX, BlueTOAD (Bluetooth Travel-time Origination and Destination), and HERE. Benchmark data were collected with the use of an instrumented vehicle at five freeway segments and two arterial segments in Florida. The field-measured travel times were statistically compared with the travel times provided through various methods. The results suggest that the HERE traffic data provide better freeway travel time estimates compared to the remaining methods. In oversaturated conditions, STEWARD, INRIX and BlueTOAD data seem to underestimate travel times, while HERE data were found to be more accurate. For undersaturated freeways, STEWARD, INRIX and BlueTOAD were found to perform better than HERE. At the arterial sites BlueTOAD and HERE travel time data were analyzed and the analysis suggests that none of the methods is accurate, possibly due to the small sample size. [ABSTRACT FROM AUTHOR]

The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective.

Mp-matching floating car data is a fundamental task in traffic surveillance, traffic anomaly detection, and urban dynamic analysis. This study proposes a parallel map-matching approach to process streaming large volume floating car data. Considering the connectivity of a transportation network, the matching candidates are limited with a coarse spatial grid. A distance filter and a direction filter are combined to reduce the number of matching candidates. The trajectory between consecutive nodes is recovered with a shortest path list. The shortest path list in memory was developed to reduce the computation and speed up the matching process. A non-relational distributed database parallelizes the map-matching procedure. The performance of the presented approach was tested with large volume floating car data in Wuhan, China. It demonstrates that this method achieves 90.62% correct map-matching results. This efficiency could fulfill the needs of real-time traffic monitoring, and will benefit trajectory analysis. [ABSTRACT FROM AUTHOR]

This paper presents a probability distribution model of vehicles' travel time index (TTI) in urban road segments that depicts traffic flow dispersion. Compared with traditional models, it can depict traffic flow dispersion with a uniform model under conditions of different road speeds or road lengths. Based on it, a dynamic model that depicts how the TTI distribution parameters vary with speed of road segment is proposed. Furthermore, parameters of the dynamic model are used to construct a characteristic vector to reveal a new inherent feature of road segments. The tests of goodness of fit for the two proposed models were carried out with data collected from 66,000 floating cars. The test area covered 40,400 urban road segments in the urban area of Beijing. Test results showed that more than 98% of the tested bins accepted the proposed TTI probability distribution model, and more than 90% of the tested road segments accepted the proposed dynamic model. The proposed models would be applied in traffic simulation, congestion forecast, signal control, and other relevant fields in the future. [ABSTRACT FROM AUTHOR]

Floating car- and loop detector-based methods are two different types of methods frequently used to collect travel time delay information across a freeway network. Sometimes, it is necessary to use them jointly to achieve the necessary freeway network coverage, due to the high labor costs for the floating car-based method and the indispensability of sufficient network instrumentation for the loop detector-based method. For example, both floating car- and loop detector-based methods were once used in the Highway Congestion Monitoring Program in the California Department of Transportation. It is therefore necessary to evaluate whether these two types of methods estimate similarly in terms of total travel time delay. To this end, corresponding delay information estimated using both types of methods from 37 freeway segments in the Greater Sacramento Area were collected and compared. It was found that these two types of methods do not estimate similarly in terms of total segment travel time delay. The mean absolute relative difference (MARD) can be as high as 78%, especially when delay is defined using a lower reference speed, such as 56 km/h. However, in terms of total segment travel time, the loop detector and the modified floating car method estimated similarly. The MARD is 19%. It was also found that the estimation from the different methods did correlate fairly well, which provides a means of conversion when different methods are used to monitor the total delay across a freeway network. As a spin-off, it was also found that a 1.5 km spacing of loop detectors is sufficient to achieve the 19% MARD as compared with the modified floating car method in terms of total travel time estimation.

Traffic oscillation is a very important phenomenon in traffic flow studies and its characteristics are usually used to validate the developed traffic flow models and theories. In this paper, we firstly conduct an empirical study based on floating car data, focusing on the characteristic of traffic oscillations in terms of the overall amplitude and the spectrum of speed/acceleration as well as the relation between the characteristic quantities. It is found that the amplitude of both speed oscillations and acceleration oscillations grows with the increase of traffic speed. The spectrum analysis shows that the most energy would concentrate on a narrower band with the increase of standard deviation of speed/acceleration, and the periodicity in the speed oscillation is more salient than that in the corresponding acceleration oscillation. Then, three typical car-following models are tested: one is the deterministic model, while the other two are the stochastic models with different mechanisms of stochasticity. The simulation results indicate that the two stochastic models outperform the deterministic model in reproducing the observed oscillation characteristics qualitatively and quantitatively. • The collected floating car data with high accuracy provides pinpoint evidence for the characteristic of traffic oscillations. • The IDM fails to reproduce qualitatively observed oscillations, while the 2D-IDM and SSAM could. • The formation and propagation of oscillations might be due to the stochasticity in car following. [ABSTRACT FROM AUTHOR]

The data collected by floating cars is an important source for lane-level map production. Compared with other data sources, this method is a low-cost but challenging way to generate high-accuracy maps. In this paper, we propose a data correction algorithm for low-frequency floating car data. First, we preprocess the trajectory data by an adaptive density optimizing method to remove the noise points with large mistakes. Then, we match the trajectory data with OpenStreetMap (OSM) using an efficient hierarchical map matching algorithm. Lastly, we correct the floating car data by an OSM-based physical attraction model. Experiments are conducted exploiting the data collected by thousands of taxies over one week in Wuhan City, China. The results show that the accuracy of the data is improved and the proposed algorithm is demonstrated to be practical and effective. [ABSTRACT FROM AUTHOR]