A four-lane model is put forward to explore the effects on vehicular fuel consumption of a freeway work zone which has a length of 100 m and occupies lanes III and IV. The model equations are solved numerically in the simulation of vehicular flows by a method called WENO5–RK3. A vehicular fuel consumption model is presented, with an additional fuel consumption defined in comparison with the case in the absence of a work zone. Simulation results show that the freeway work zone affects vehicular fuel consumption significantly, triggers a traffic jam as soon as initial density normalized by jam density is beyond 0.18.
HuangZMSmirnovaMNBiJ, et al. Analyzing roadway work zone effects on vehicular flow in a freeway ring. Int J Modern Phys C2023; 34: 2350051.
2.
CassidyMRudjanakanoknadJ. Increasing the capacity of an isolated merge by metering its on-ramp. Transp Res Part B Methodol2005; 39: 896–913.
3.
PatireADCassidyMJ. Lane changing patterns of bane and benefit: observations of an uphill expressway. Transp Res Part B Methodol2011; 45: 656–666.
4.
ZhengZAhnSChenD, et al. Freeway traffic oscillations: microscopic analysis of formations and propagations using Wavelet Transform. Transp Res Part B Methodol2011; 45: 1378–1388.
5.
ZhengZD. Recent developments and research needs in modeling lane changing. Transp Res Part B Methodol2014; 60: 16–32.
6.
AliYZhengZDHaqueMM, et al. A game theory-based approach for modelling mandatory lane changing behaviour in a connected environment. Transp Res Part C Emerg Technol2019; 106: 220–242.
7.
GippsPG. A model for the structure of lane-changing decisions. Transp Res Part B Methodol1986; 20: 403–414.
8.
YangQKoutsopoulosHN. A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transp Res Part C Emerg Technol1996; 4: 113–129.
9.
HidasP. Modelling lane changing and merging in microscopic traffic simulation. Transp Res Part C Emerg Technol2002; 10: 351–371.
10.
KestingATreiberMHelbingD. General lane-changing model MOBIL for car-following models. Transp Res Rec J Transp Res Board2007; 1999: 86–94.
11.
TianSXuKMaMN. Lane changing behavior of vehicles in urban road work zone based on cellular automata. J Chongqing Jiaotong Univ (Nat Sci)2019; 38: 113–118.
12.
DimitropoulosIKanellaidisG. An international review on road work safety. In: Traffic safety on two continents, Malmo, 20–22 September 1999.
13.
YangCHParkSHKimJG, et al. Traffic operation analysis for underground and ground roads using microscopic traffic simulation. SIMULATION2022; 98: 1071–1082.
14.
WangXSLiuTXuM. Bus bunching simulation based on the lattice Boltzmann model of traffic flow. SIMULATION2022; 99: 183–199.
15.
LiZMSmirnovaMNZhangYL, et al. Numerical exploration of freeway tunnel effects with a two-lane traffic model. SIMULATION2022; 99: 55–68.
16.
LiSSmirnovaMNYangSJ, et al. Exploring the effects of work zone on vehicular flow in ring freeway with a tunnel by a three-lane continuum model. Int J Transp Sci Technol. Epub ahead of print March23, 2023. DOI: 10.1016/j.ijtst.2023.03.004.
17.
LiZMSmirnovaMNZhangYL, et al. Tunnel speed limit effects on traffic flow explored with a three lane model. Math Comput Simul2022; 194: 185–197.
JohnsenEColoniusT. Implementation of WENO schemes in compressible multicomponent flow problems. J Comput Phys2006; 219: 715–732.
29.
HuZJSmirnovaMNSmirnovNN, et al. Impacts of down-up hill segment on the threshold of shock formation of ring road vehicular flow. Adv Appl Math Mech2023; 15: 1315–1334.
30.
KoshiMKuwaharaMAkahaneH. Capacity of sags and tunnels on Japanese motorways. ITE J1992; 62: 17–22.
31.
JinWL. Kinematic wave models of sag and tunnel bottlenecks. Transp Res Part B Methodol2018; 107: 41–56.
