An Improved Deep Learning Model for Traffic Crash Prediction

Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively. Document type: Articl

Exploring the future Electric Vehicle market and its impacts with an agent-based spatial integrated framework: A case study of Beijing, China

This paper investigates the potential expansion and impacts of Electric Vehicle (EV) market in Beijing, China at the micro level with an agent-based integrated urban model (SelfSim-EV), considering the interactions, feedbacks and dynamics found in the complex urban system. Specifically, a calibrated and validated SelfSim-EV Beijing model was firstly used to simulate how the EV market might expand in the context of urban evolution from 2016 to 2020, based on which the potential impacts of EV market expansion on the environment, power grid system and transportation infrastructures were assessed at the multiple resolutions. The results suggest that 1) the adoption rate of Battery Electric Vehicle (BEV) increases over the period, whereas the rate of Plug-in Hybrid Electric Vehicle (PHEV) almost remains the same; Furthermore, the so-called neighbour effects appear to influence the uptake of BEVs, based on the spatial analyses of the residential locations of BEV owners; 2) the EV market expansion could eventually benefit the environment, as evident from the slight decrease in the amounts of HC, CO and CO2 emissions after 2017; 3) Charging demand accounting for around 4% of total residential electricity demand in 2020 may put slight pressure on the power grid system; 4) the EV market expansion could influence several EV-related transport facilities, including parking lots, refuelling stations, and charging posts at parking lots, in terms of quantity, layout and usage. These results are expected to be useful for different EV-related stakeholders, such as local authorities and manufacturers, to shape polices and invest in technologies and infrastructures for EVs

The role of the license plate lottery policy in the adoption of Electric Vehicles: A case study of Beijing

Policy is an influential factor to the purchase and usage of Electric Vehicles (EVs). This paper is focused on the license plate lottery policy, a typical vehicle purchase restriction in Beijing, China. An agent-based spatial integrated urban model, SelfSim-EV, is employed to investigate how the policy may influence the uptake of EVs over time at the individual level. Two types of “what-if” scenario were set up to explore how the methods to allocate the vehicle purchase permits and the number of permits might influence the EV market expansion from 2016 to 2020. The results suggested that 1) both the allocation methods and the number of purchase permits could heavily influence the uptake of EVs and further its impacts on vehicular emissions, energy consumption and urban infrastructures; 2) compared to the baseline, both scenarios got significantly different spatial distributions of vehicle owners, transport facilities, vehicular emissions and charging demand at the multiple resolutions; 3) SelfSim-EV was found as a useful tool to quantify the nonlinear relationships between the increase of EV purchasers and the demand for transport facilities and electricity, and also to capture some unexpected results coming out from the interactions in the complex dynamic urban system

Kalman filter algorithm for short-term jam traffic prediction based on traffic parameter correlation

A Kalman filter model considering the correlation property of traffic flow parameters is proposed to realize network short-term traffic flow prediction under jam traffic. The proposed state-space model of short-term traffic flow prediction is presented by solving the conservation equation using Lax-Wendroff scheme. In addition, the spatial-temporal characteristics of the traffic flow on urban expressway networks and the influence factors of on and off ramp are taken into account for flow rate prediction. The estimation algorithm of the proposed state-space model is designed based on the Kalman filter method. A region expressway network in Beijing is taken as an example to evaluate the performance of the proposed method. The results show that the maximum prediction mean absolute percentage error (MAPE) of the proposed Kalman filter model is less than 10% since the input of the Kalman filter model considers the impacts the spatial-temporal characteristics, and the mean of prediction MAPE is 7.96%. For the same predicted conditions, the mean prediction MAPEs of ARIMA and Elman model are 19.88% and 10.51%, respectively

Traffic assignment model and algorithm with combined modes in a degradable transportation network

To study the effect of the degradable road network on travel decision and traffic assignment, firstly all travelers were categorized into three classes: risk-averse travelers, risk-prone travelers and risk-neutral travelers. Secondly, two travel modes including car trip and car-subway trip were considered in a multi-modal networks. A stochastic traffic equilibrium model was proposed by using the variational inequality theory, and the properties of the solution were discussed. Finally, an example was used to testify the validity of the model and the algorithm. The results show that the travel behavior varies greatly with different travel time budget; with the decrease of traffic capacity, travelers prefer to choose the combined modes

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Traffic Volume Prediction: A Fusion Deep Learning Model Considering Spatial–Temporal Correlation

In this paper, a fusion deep learning model considering spatial–temporal correlation is proposed to solve the problem of urban road traffic flow prediction. Firstly, this paper holds that the traffic flow of a section in the urban road network not only depends on the fluctuation of its own time series, but is also related to the traffic flow of other sections in the whole region. Therefore, a traffic flow similarity measurement method based on wavelet decomposition and dynamic time warping is proposed to screen the sections which are similar to the traffic flow state of the target section. Secondly, in order to improve the prediction accuracy, the unstable time series are reconstructed into stationary time series by differential method. Finally, taking the extracted traffic flow data of a similar section as an independent variable and the traffic flow data of target section as dependent variable, we input the above variables into the proposed CNN-LSTM fusion deep learning model for traffic flow prediction. The results show that the proposed model has a higher accuracy and stability than the other benchmark models. The MAPE can reach 92.68%, 93.39%, 85.14%, and 76.14% at a time interval of 5 min, 15 min, 30 min, and 60 min, and the other evaluation indexes are also better than the rest of the benchmark models

Analysis and Prediction Model of Resident Travel Satisfaction

To promote the sustainable development of urban traffic and improve resident travel satisfaction, the significant factors affecting resident travel satisfaction are analyzed in this paper. An evaluation and prediction model for travel satisfaction based on support vector machine (SVM) is constructed. First, a multinomial logit (MNL) model is constructed to reveal the impact of individual attributes, family attributes and safety hazards on resident travel satisfaction and to clarify the significant factors. Then, a travel satisfaction evaluation model based on the SVM is constructed by taking significant factors as independent variables. Finally, travel optimization measures are proposed and the SVM model is used to predict the effect. Futian Street in Futian District of Shenzhen is taken as the object to carry out specific research. The results show that the following factors have a significant effect on resident travel satisfaction: age, job, level of education, number of car, income, residential area and potential safety hazards of people, vehicles, roads, environment, etc. The model fitting accuracy is 87.76%. The implementation of travel optimization measures may increase travel satisfaction rate by 14.07%

A simulation-based dynamic traffic assignment model with combined modes

This paper presents a dynamic traffic assignment (DTA) model for urban multi-modal transportation network by con­structing a mesoscopic simulation model. Several traffic means such as private car, subway, bus and bicycle are con­sidered in the network. The mesoscopic simulator consists of a mesoscopic supply simulator based on MesoTS model and a time-dependent demand simulator. The mode choice is si­multaneously considered with the route choice based on the improved C-Logit model. The traffic assignment procedure is implemented by a time-dependent shortest path (TDSP) al­gorithm in which travellers choose their modes and routes based on a range of choice criteria. The model is particularly suited for appraising a variety of transportation management measures, especially for the application of Intelligent Trans­port Systems (ITS). Five example cases including OD demand level, bus frequency, parking fee, information supply and car ownership rate are designed to test the proposed simulation model through a medium-scale case study in Beijing Chaoy­ang District in China. Computational results illustrate excel­lent performance and the application of the model to analy­sis of urban multi-modal transportation networks

Exploring the effects of the built environment on commuting mode choice in neighborhoods near public transit stations: evidence from China

Public transport services are often improved to decrease auto-related mobility. However, it remains unclear how the built environment influences auto ownership and commuting mode choice near public transit stations. Using 3318 individuals across China, this study contributes to investigating the relationship between the built environment, auto ownership and commuting mode choice simultaneously in neighborhoods near public transit stations by employing an integration of a structural equation model (SEM) and discrete choice model (DCM). The results indicate that the built environment exhibits significant impacts on auto-related mobility near public transit stations and auto ownership plays an intermediary role between the exogenous variables and commuting mode choice, thus it can moderate the influences of built environment attributes on commuting mode choice. The results suggest that increasing green coverage rates and promoting diverse land use development strategies in transit-oriented development (TOD) areas may make contributions to auto-related mobility reduction and low-carbon travel initiatives