Impacts of Bus Lane on Bus Travel Time Reliability:a Case Study in Shenzhen

The aim of the paper is to evaluate the impacts of bus lane on bus travel time reliability.The data used are the Geographic Positioning System(GPS) data of two bus lines running parallel streets in Shenzhen,China,one of which is a bus lane and the other is a regular lane.Two linear regression models are developed to evaluate the influence of bus lane which has a separated right of way.Other factors including running direction,day of week,time of day,dwell time,and delay at the start point are also considered in the model.Without published time tables,coefficient of variance(CV) of travel time is employed to explore the impacts of bus lane.The results indicate that bus lane can save 22.0% of travel time,reduce 11.5% of the CV of travel time,and decrease the variance of headway by 17.4%.The analysis on bus travel time reliability could help operators and drivers improve the quality of transit service.It also sheds light on how to assess the effectiveness of bus lane for transit planners and service operators.

A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology

The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short-term travel time forecasting on urban arterials,a prediction model( PSOSVM) combining support vector machine( SVM) and particle swarm optimization( PSO) is developed. Travel time data collected with Bluetooth devices are used to calibrate the proposed model. Field experiments show that the PSO-SVM model 's error indicators are lower than the single SVM model and the BP neural network( BPNN) model. Particularly,the mean-absolute percentage error( MAPE) of PSO-SVM is only 9. 453 4 %which is less than that of the single SVM model( 12. 230 2 %) and the BPNN model( 15. 314 7 %). The results indicate that the proposed PSO-SVM model is feasible and more effective than other models for shortterm travel time prediction on urban arterials.

Travel time prediction with viscoelastic traffic model

Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.

Evaluation of floating car technologies for travel time estimation

Travel times have been traditionally estimated from data collected by roadway sensors. Recently, new tech- nologies, such as cell phone tracking, license plate matching, automatic vehicle identifications and video detection, are employed for this purpose. In this study, the data collected by TRANSMIT readers, Bluetooth sensors, and INRIX are assessed by comparing each to the ＂ground truth＂ travel times collected by probe vehicles carrying GPS-based naviga- tion devices. Travel times of probe vehicles traveling on the study segment of 1-287 in New Jersey were collected in 2009. Statistical measures, such as standard deviation, average absolute speed error, and speed error bias, were used to make an in-depth analysis. The accuracy of each travel time estimation method is analyzed. The data collected by Bluetooth sensors and the TRANSMIT readers seem more consistent with the ground true data, and slightly outperform the data reported by 1NRIX. This study established a procedure for analyzing the accuracy of floating car data （FCD） collected by different technologies.

Optimization of traffic signal parameters based on distribution of link travel time

In order to make full use of digital data, such as data extracted from electronic police video systems, and optimize intersection signal parameters, the theoretical distribution of the vehicle's road travel time must first be determined. The intersection signal cycle and the green splits were optimized simultaneously, and the system total travel time was selected as the optimization goal. The distribution of the vehicle's link travel time is the combined results of the flow composition, road marking, the form of control, and the driver's driving habits. The method proposed has 15% lower system total stop delay and fewer total stops than the method of TRRL(Transport and Road Research Laboratory) in England and the method of ARRB(Australian Road Research Board) in Australia. This method can save 0.5% total travel time and will be easier to understand and test, which establishes a causal relationship between optimal results and specific forms of road segment management, such as speed limits.

Bus travel time reliability incorporating stop waiting time and in‑vehicle travel time with AVL data

Improving bus travel time reliability can attract more commuters to use bus transit,and therefore reduces the share of cars and alleviates trafc congestion.This paper formulates a new bus travel time reliability metric that jointly considers two stochastic processes:the in-stop waiting process and in-vehicle travel time process,and the bus travel time reliability function is calculated by the convolution of independent events’probabilities.The new reliability metric is defned as the probability when bus travel time is less than a certain threshold and can be used in both conditions with and without bus transfer.Next,Automatic Vehicle Location(AVL)data of the city of Harbin is used to demonstrate the applicability of the proposed method.Results show that factors such as weather,day of the week,departure time,travel distance,and the distance from the boarding stop to the bus departure station can signifcantly afect the travel time reliability.Then,a case with low bus departure frequency is analyzed to show the impact of travelers’arrival distribution on their bus travel time reliability.Further,it is demonstrated that the travel time reliabilities of two bus transfer schemes of the same Origin–Destination(O–D)pair can have signifcantly diferent patterns.Understanding the bus travel time reliability pattern of the alternative bus routes can help passengers to choose a more reliable bus route under diferent conditions.The proposed bus travel time reliability metric is tested to be sensitive to the efect of diferent factors and can be applied in bus route recommendation,bus service evaluation,and optimization.

2-D crustal Poisson′s ratio from seismic travel time inversion in Changbaishan Tianchi volcanic region

Based on the inversion method of 2D velocity structure and interface, the crustal velocity structures of P-wave and S-wave along the profile L1 are determined simultaneously with deep seismic sounding data in Changbaishan Tianchi volcanic region, and then its Poisson's ratio is obtained. Calculated results show that this technique overcomes some defects of traditional forward calculation method, and it is also very effective to determine Poisson's ratio distribution of deep seismic sounding profile, especially useful for study on volcanic magma and crustal fault zone. Study result indicates that there is an abnormally high Poisson's ratio body that is about 30 km wide and 12 km high in the low velocity region under Tianchi crater. Its value of Poisson's ratio is 8% higher than that of surrounding medium and it should be the magma chamber formed from melted rock with high temperature. There is a high Poisson's ratio zone ranging from magma chamber to the top of crust, which may be the uprise passage of hot substance. The lower part with high Poisson's ratio, which stretches downward to Moho, is possibly the extrusion way of hot substance from the uppermost mantle. The conclusions above are consistent with the study results of both tomographic determination of 3D crustal structure and magnetotelluric survey in this region.

Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey

Travel time estimation is an integral part of Intelligent Transportation Systems, and has been an important component in traffic management and operations for many years. Travel time, being spatial in nature, requires spatial sensors to measure it accurately. Bluetooth is emerging as a promising technology for the direct measurement of travel time, and is reported in a few studies from homogenous traffic conditions. At the same time, there have been no studies on the applicability of Bluetooth for travel time estimation in heterogeneous traffic seen in Istanbul and even that Turkey. Bluetooth data collected from a busy urban road in Istanbul city have been analyzed and the penetration rate was found to be about 5 %. Two wheelers and light motor vehicles have been detected using the Bluetooth sensor and the data have been extrapolated to estimate travel times of other classes of vehicles. The study developed linear relationships between speeds of different classes of vehicles through weighted linear regression methods and were used for the estimation of stream travel time. The results obtained were promising and show that Bluetooth is a cost-effective technology for estimation of travel time for heterogeneous traffic conditions.

Observations and modelling of the travel time delay and leading negative phase of the 16 September 2015 Illapel,Chile tsunami

The systematic discrepancies in both tsunami arrival time and leading negative phase(LNP)were identified for the recent transoceanic tsunami on 16 September 2015 in Illapel,Chile by examining the wave characteristics from the tsunami records at 21 Deep-ocean Assessment and Reporting of Tsunami(DART)sites and 29 coastal tide gauge stations.The results revealed systematic travel time delay of as much as 22 min(approximately 1.7%of the total travel time)relative to the simulated long waves from the 2015 Chilean tsunami.The delay discrepancy was found to increase with travel time.It was difficult to identify the LNP from the near-shore observation system due to the strong background noise,but the initial negative phase feature became more obvious as the tsunami propagated away from the source area in the deep ocean.We determined that the LNP for the Chilean tsunami had an average duration of 33 min,which was close to the dominant period of the tsunami source.Most of the amplitude ratios to the first elevation phase were approximately 40%,with the largest equivalent to the first positive phase amplitude.We performed numerical analyses by applying the corrected long wave model,which accounted for the effects of seawater density stratification due to compressibility,self-attraction and loading(SAL)of the earth,and wave dispersion compared with observed tsunami waveforms.We attempted to accurately calculate the arrival time and LNP,and to understand how much of a role the physical mechanism played in the discrepancies for the moderate transoceanic tsunami event.The mainly focus of the study is to quantitatively evaluate the contribution of each secondary physical effect to the systematic discrepancies using the corrected shallow water model.Taking all of these effects into consideration,our results demonstrated good agreement between the observed and simulated waveforms.We can conclude that the corrected shallow water model can reduce the tsunami propagation speed and reproduce the LNP,which is observed for tsunamis that have propagated over long distances frequently.The travel time delay between the observed and corrected simulated waveforms is reduced to<8 min and the amplitude discrepancy between them was also markedly diminished.The incorporated effects amounted to approximately 78%of the travel time delay correction,with seawater density stratification,SAL,and Boussinesq dispersion contributing approximately 39%,21%,and 18%,respectively.The simulated results showed that the elastic loading and Boussinesq dispersion not only affected travel time but also changed the simulated waveforms for this event.In contrast,the seawater stratification only reduced the tsunami speed,whereas the earth’s elasticity loading was responsible for LNP due to the depression of the seafloor surrounding additional tsunami loading at far-field stations.This study revealed that the traditional shallow water model has inherent defects in estimating tsunami arrival,and the leading negative phase of a tsunami is a typical recognizable feature of a moderately strong transoceanic tsunami.These results also support previous theory and can help to explain the observed discrepancies.

Analysis of travel time, sources of water and well protection zones with groundwater models

This study compares numerical models with analytical solutions in computing travel times and radius of protection zones for a pumping well located in an unconfined aquifer with uniform recharge and in a semi-confined aquifer. Numerical models were capable of delineating protection zones using particle tracking method in both cases. However, protection zones defined by travel time criterion can only protect small percent of source water to the well; large percent of source water is not protected which may pose a risk of pollution of source water to the well. The case study of Leggeloo well field in the Netherlands indicates that although a well field protection area was enforced in 1980 s, elevated nitrate concentration has been monitored in the abstracted water since 1990 s. The analysis of protection areas shows that the current protection area only protects 37.4% of recharge water to the well field. A large protection area must be adopted in order to safeguard the sustainable water supply for the local community.

Measuring PmP travel times using teleseismic S-wave waveform data

Joint inversion for crustal velocity structure using only surface wave dispersion and receiver function data often suffers the non-unique solution problem due to lack of P wave velocity constraint in the data.Here we developed a method for measuring PmP travel time using teleseismic S wave waveform.The major improvement by this method over the previous one-layer-crust search method is its use of a more realistic multi-layer-crust(MLC)velocity model in the study region based on available information.Numerical tests show that compared with the previous search method the MLC search method is faster and more reliable and accurate.One limit of the MLC search method is the requirement of a multi-layer background model,thus this method might not be applicable in regions where the velocity structure is unknown.Nevertheless,our numerical tests show that the MLC search method is robust in the sense that small deviations of the background model from the true velocity model do not influence the results severely.We applied the MLC search method to data from a temporary linear array across the Wabash Valley Seismic Zone in the central USA.We obtained 157 PmP travel-time measurements that show lateral crustal structure variation in the region.The measurements provide additional constraints in joint inversion for crustal velocity structure in this seismically active region.

Signal Travel Time Anomaly between Earth and Solar Planets

Travel time anomaly in the communicatin of spatial instruments installed in solar planets and Earth are investigated in a model of gravity impact on radio signal propagation. Resulting travel times anomaly shows variations less than 10-3 seconds to and from Venus and Mars while Sun provides anomaly travel times of about -2 × 10-2 sec as backward signal needs more times than emitted signal from Earth. In the current explorations on Mars, the travel time anomaly may reach 1.8 × 10-4 sec as orbital Mars position pass through its minimum distance with respect to Earth. Implications of the difference between one and two-way travel times may be related to redshift/blueshift while travel time of received signal is less or greater than emitted signal.

Alpha-reliable combined mean traffic equilibrium model with stochastic travel times

Based on the reliability budget and percentile travel time(PTT) concept, a new travel time index named combined mean travel time(CMTT) under stochastic traffic network was proposed. CMTT here was defined as the convex combination of the conditional expectations of PTT-below and PTT-excess travel times. The former was designed as a risk-optimistic travel time index, and the latter was a risk-pessimistic one. Hence, CMTT was able to describe various routing risk-attitudes. The central idea of CMTT was comprehensively illustrated and the difference among the existing travel time indices was analyzed. The Wardropian combined mean traffic equilibrium(CMTE) model was formulated as a variational inequality and solved via an alternating direction algorithm nesting extra-gradient projection process. Some mathematical properties of CMTT and CMTE model were rigorously proved. Finally, a numerical example was performed to characterize the CMTE network. It is founded that that risk-pessimism is of more benefit to a modest(or low) congestion and risk network, however, it changes to be risk-optimism for a high congestion and risk network.

An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

旅行时间的短期的预报为聪明的交通系统的成功是必要的。在这份报纸，我们考察预报模型的短期的交通的 state-of-art 并且构画出他们每个模型的基本想法，相关工作，优点和劣势。一改进适应指数的变光滑(IAES ) 模型也被建议克服以前的适应指数的变光滑模型的缺点。然后，比较实验在状况和反常交通调节评估在牌照匹配获得的直接旅行时间数据(每分钟行数) 上预报模型的四个主要分支的性能的正常交通下面被执行。实验的结果证明每个模型似乎有它的自己的力量和软弱。IASE 的预报表演比在更突然预报地平线(预报的和二步) 的另外的模型优异， IASE 能够处理各种交通条件。

Getting pre-stack time migration travel times from the single square root operator

改进集中的能力预先叠时间移植允许想象的节反映结构的特征，深度，和接口形状，它是为起始的深度迁居速度模型的准备的关键步。传统的对称的旅行时间方程基于一个分层的模型的假设被导出。完成与强壮的侧面的变化集中在媒介的需要的效果是困难的。非对称的旅行时间方程基于谎言代数学和一个伪 differential 操作符包含能甚至在强烈侧面的可变媒介并且也改进集中的能力的侧面的速度衍生物为相对振幅保藏的重量系数的计算精确。与对称的方法相比，非对称的方法是更有效的。在这篇论文，我们描述几关键步非对称预先叠旅行时间计算和现在的一些测试用合成、真实的数据结果。

Robust user equilibrium model based on cumulative prospect theory under distribution-free travel time

The assumption widely used in the user equilibrium model for stochastic network was that the probability distributions of the travel time were known explicitly by travelers. However, this distribution may be unavailable in reality. By relaxing the restrictive assumption, a robust user equilibrium model based on cumulative prospect theory under distribution-free travel time was presented. In the absence of the cumulative distribution function of the travel time, the exact cumulative prospect value(CPV) for each route cannot be obtained. However, the upper and lower bounds on the CPV can be calculated by probability inequalities.Travelers were assumed to choose the routes with the best worst-case CPVs. The proposed model was formulated as a variational inequality problem and solved via a heuristic solution algorithm. A numerical example was also provided to illustrate the application of the proposed model and the efficiency of the solution algorithm.

Integrated reliability of travel time and capacity of urban road network under ice and snowfall conditions

In order to evaluate and integrate travel time reliability and capacity reliability of a road network subjected to ice and snowfall conditions,the conceptions of travel time reliability and capacity reliability were defined under special conditions.The link travel time model(ice and snowfall based-bureau public road,ISB-BPR) and the path choice decision model(elastic demand user equilibrium,EDUE) were proposed.The integrated reliability was defined and the model was set up.Monte Carlo simulation was used to calculate the model and a numerical example was provided to demonstrate the application of the model and efficiency of the solution algorithm.The results show that the intensity of ice and snowfall,the traffic demand and supply,and the requirements for level of service(LOS) have great influence on the reliability of a road network.For example,the reliability drops from 65% to 5% when the traffic demand increases by 30%.The comprehensive performance index may be used for network planning,design and maintenance.

Study on the Influence of Airgun Excitation Conditions on Airgun Signals and Travel Time Variation Measurements

Because of the different excitation conditions of the airgun source,there will be subtle differences in airgun signals.Travel time variation of airgun signals often mix into source information which can’t fully reflect the evolution of the medium.This article uses the airgun signals from the Binchuan Transmitting Seismic Station to analyze the airgun signal′s characteristics of phase and correlation.We conducted a comparative analysis of the effects of the pair difference method and the deconvolution method on eliminating the influence of the excitation conditions in travel time variation.The results show that:(1)The pressure pulse and its subsequent wave of airgun source wavelet are less affected by excitation conditions that we can use it to obtain high-precision excitation moments.(2)Deconvolution can improve the correlation of the airgun signal.(3)The pair difference method can’t eliminate the influence of excitation conditions in travel time variation.Deconvolution can reduce excitation condition interference and the influence of the excitation condition in the travel time variation after deconvolution of the vertical signal is significantly reduced.

Support Vector Regression for Bus Travel Time Prediction Using Wavelet Transform

In order to accurately predict bus travel time, a hybrid model based on combining wavelet transform technique with support vector regression(WT-SVR) model is employed. In this model, wavelet decomposition is used to extract important information of data at different levels and enhances the forecasting ability of the model. After wavelet transform different components are forecasted by their corresponding SVR predictors. The final prediction result is obtained by the summation of the predicted results for each component. The proposed hybrid model is examined by the data of bus route No.550 in Nanjing, China. The performance of WT-SVR model is evaluated by mean absolute error(MAE), mean absolute percent error(MAPE) and relative mean square error(RMSE), and also compared to regular SVR and ANN models. The results show that the prediction method based on wavelet transform and SVR has better tracking ability and dynamic behavior than regular SVR and ANN models. The forecasting performance is remarkably improved to obtain within 6% MAPE for testing section Ⅰ and 8% MAPE for testing section Ⅱ, which proves that the suggested approach is feasible and applicable in bus travel time prediction.

Stochastic network equilibrium model with reliable travel time confidence level

In order to ensure on-time arrival when travelersmake their trips, the stochastic network assignment modelunder uncertainty of travel time is investigated. First, basedon travelers＇ route choice behavior, the reliable travel timeconfidence level （RTTCL）, which is the probability that a triparrives within the shortest average travel time plus theacceptable travel time difference, is defined. Then, areliability-based user equilibrium （RUE） model, whichhypothesizes that for each OD pair no traveler can improvehis/her RTTCL by unilaterally changing routes, is built.Since the traditional traffic assignment algorithms are notfeasible to solve the RUE model, a quasi method of successiveaverage （QMSA） is developed. Using Nguyen-Dupuis andSioux Falls networks, the model and the algorithm are tested.The results show that the QMSA algorithm can rapidlyconverge to a high accuracy for solving the proposed RUEmodel, and the RUE model can provide a good response totravelers＇ behavior in the stochastic network.