The MultiType Demands Oriented Framework for FlexRoute Transit Design
Abstract
:1. Introduction
 (1)
 Compared with the singlefeature classification method, this paper proposes MFCM to evaluate multiple kinds of station features, which performs better in recognizing the differences between stations.
 (2)
 Instead of predesigning base routes, we generate a multiroute plan by flexibly integrating reserved demands and regular travel patterns. TSPM can serve 20% more demands with less traveling time per passenger than the baseroute predesigned planning method.
 (3)
 Numerical experiments prove the applicability of FT and show that, compared with traditional transit, our proposed FT transports demands more effectively and saves nearly 40% of the total cost.
2. MultiType Demands Oriented Framework
2.1. MultiFeatureBased Classification Method (MFCM)
2.2. TwoStage Planning Method (TSPM)
 (1)
 Regardless of current demands, a base route is designed to visit all static stations with minimum distance.
 (2)
 Next, focusing on reserved demands, the bus can serve Demand Ⅰ since the constraints of passenger waiting time and bus singletrip time are both satisfied. An offline plan is generated by inserting dynamic station ⑧ into the base route.
 (3)
 Then, considering realtime demands, Demand Ⅱ is rejected for the reason that deviating to dynamic station ⑥ would exceed the maximum waiting time of Demand Ⅱ. Thus, the online plan remains the same as the offline plan with 30 minutes of the bus singletrip time.
 (1)
 With the flexible combination of reserved demands and regular travel patterns, Demand Ⅰ is served. And the offline plan visits all static stations and dynamic station ⑧ within an acceptable distance.
 (2)
 Then, considering realtime demands, the online plan accepts Demand Ⅱ with the acceptable waiting time and little deviation to dynamic station ⑥, with 27 minutes of the bus singletrip time.
3. MultiRoute Design Model (MRDM) and Its Solution
3.1. Model Assumptions
 (1)
 The volume of a bus is fixed.
 (2)
 The number of available buses is unlimited.
 (3)
 The bus dispatch is not considered.
 (4)
 The bus ticket is free.
 (5)
 The driving time between two stations is known beforehand and set as the minimum value according to the shortest path.
 (6)
 The travel plans of reserved demands and realtime demands are known, including the origin station, the destination, and the demand time.
 (7)
 The bus arrival time at a station is equal to the boarding time of accepted passengers whose origin is the same station.
3.2. Model Formulation
 Objective function:
 Trip constraints:
 Demand constraints:
 FT service constraints:
 Decision variables:
3.3. Model Solution
Algorithm 1. Routedesignoriented genetic algorithm 
Input: the population size ${N}_{POPUL}$, the maximum iterations ${N}_{ITER}$, and others 

Output: the global best value $fi{t}_{best}$ and the global best individual $in{d}_{best}$ 
 Population initialization.
 Individual evaluation and optimization.
 Iteration termination.
 Individual decoding.
Algorithm 2. Termination judgement 
Input: $fi{t}_{best},in{d}_{best},fi{t}_{iter,best},in{d}_{iter,best},count$, the threshold ${\Delta}_{ITER},$ and others 

Output: the global best value $fi{t}_{best}$, the global best individual $in{d}_{best}$, and $count$ 
4. Route Modification Model (RMM) and Its Solution
4.1. Model Formulation
 Objective function:
 Constraints:
 Decision variables:
4.2. Model Solution
 (1)
 Input the firstreceived realtime demand and the offline plan into RMM;
 (2)
 Start greedy algorithm and initialize an online plan;
 (3)
 Search for candidate trips, of which the operational time meets the demand time;
 (4)
 Insert the related dynamic stations of the demand into each candidate trip based on the spatial and temporal similarity, and generate the adjusted plans;
 (5)
 Evaluate the adjusted plans and the online plan according to Equation (17);
 (6)
 Output the online plan with minimum cost and end greedy algorithm;
 (7)
 Input the next realtime demand and the online plan into RMM and repeat steps (2) to (6) until FT responds to all received realtime demands.
Algorithm 3. Greedy algorithm 
Input: a realtime demand ${n}^{\ast}$, the offline plan $pla{n}_{off}$, the online plan $pla{n}_{{n}^{\ast}1}$, the rejection loss ${\phi}_{4}$, and others 

Output: the online plan $pla{n}_{{n}^{\ast}}$ and the minimum cost $cos{t}_{best}$ 
5. Result Analysis
5.1. Experiment Preparation
5.2. Sensitivity Analysis
5.3. Application Analysis
5.4. Method Comparison
5.5. Adaptability Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
 Erhardt, G.D.; Hoque, J.M.; Goyal, V.; Berrebi, S.; Brakewood, C.; Watkins, K.E. Why has public transit ridership declined in the United States? Transp. Res. Part A Policy Pract. 2022, 161, 68–87. [Google Scholar] [CrossRef]
 Bakas, I.; Drakoulis, R.; Floudas, N.; Lytrivis, P.; Amditis, A. A flexible transportation service for the optimization of a fixedroute public transport network. Transp. Res. Procedia 2016, 14, 1689–1698. [Google Scholar] [CrossRef] [Green Version]
 Angelelli, E.; Morandi, V.; Speranza, M.G. Optimization models for fair horizontal collaboration in demandresponsive transportation. Transp. Res. Part C Emerg. Technol. 2022, 140, 103725. [Google Scholar] [CrossRef]
 Militão, A.M.; Tirachini, A. Optimal fleet size for a shared demandresponsive transport system with humandriven vs automated vehicles: A total cost minimization approach. Transp. Res. Part A Policy Pract. 2021, 151, 52–80. [Google Scholar] [CrossRef]
 Nickkar, A.; Lee, Y.J.; Meskar, M. Developing an optimal algorithm for demand responsive feeder transit service accommodating temporary stops. J. Public Transp. 2022, 24, 100021. [Google Scholar] [CrossRef]
 Brake, J.; Nelson, J.D. A case study of flexible solutions to transport demand in a deregulated environment. J. Transp. Geogr. 2007, 15, 262–273. [Google Scholar] [CrossRef]
 Gorev, A.; Popova, O.; Solodkij, A. Demandresponsive transit systems in areas with low transport demand of “smart city”. Transp. Res. Procedia 2020, 50, 160–166. [Google Scholar] [CrossRef]
 Ali, A.; Ayub, N.; Shiraz, M.; Ullah, N.; Gani, A.; Qureshi, M.A. Traffic efficiency models for urban traffic management using mobile crowd sensing: A survey. Sustainability 2021, 13, 13068. [Google Scholar] [CrossRef]
 Knierim, L.; Schlüter, J.C. The attitude of potentially less mobile people towards demand responsive transport in a rural area in central Germany. J. Transp. Geogr. 2021, 96, 103202. [Google Scholar] [CrossRef]
 Cao, Y.; Jiang, D.; Wang, S. Optimization for feeder bus route model design with station transfer. Sustainability 2022, 14, 2780. [Google Scholar] [CrossRef]
 Zhang, C.; Wang, M.; Dong, J.; Lu, W.; Liu, Y.; Ni, A.; Yu, X. Factors and mechanism affecting the attractiveness of public transport: Macroscopic and microscopic perspectives. J. Adv. Transp. 2022, 2022, 5048678. [Google Scholar] [CrossRef]
 Bennich, T.; Belyazid, S. The route to sustainability—Prospects and challenges of the biobased economy. Sustainability 2017, 9, 887. [Google Scholar] [CrossRef] [Green Version]
 Tong, L.; Zhou, L.; Liu, J.; Zhou, X. Customized bus service design for jointly optimizing passengertovehicle assignment and vehicle routing. Transp. Res. Part C Emerg. Technol. 2017, 85, 451–475. [Google Scholar] [CrossRef]
 Anburuvel, A.; Perera, W.U.; Randeniya, R.D. A demand responsive public transport for a spatially scattered population in a developing country. Case Stud. Transp. Policy 2022, 10, 187–197. [Google Scholar] [CrossRef]
 Schasché, S.E.; Sposato, R.G.; Hampl, N. The dilemma of demandresponsive transport services in rural areas: Conflicting expectations and weak user acceptance. Transp. Policy 2022, 126, 43–54. [Google Scholar] [CrossRef]
 Asghari, M.; Alehashem, S.M.; Rekik, Y. Environmental and social implications of incorporating carpooling service on a customized bus system. Comput. Oper. Res. 2022, 142, 105724. [Google Scholar] [CrossRef]
 Kirchhoff, P. Public transit research and development in Germany. Transp. Res. Part A Policy Pract. 1995, 29, 1–7. [Google Scholar] [CrossRef]
 Velaga, N.R.; Beecroft, M.; Nelson, J.D.; Corsar, D.; Edwards, P. Transport poverty meets the digital divide: Accessibility and connectivity in rural communities. J. Transp. Geogr. 2012, 21, 102–112. [Google Scholar] [CrossRef] [Green Version]
 Errico, F.; Crainic, T.G.; Malucelli, F.; Nonato, M. A survey on planning semiflexible transit systems: Methodological issues and a unifying framework. Transp. Res. Part C Emerg. Technol. 2013, 36, 324–338. [Google Scholar] [CrossRef]
 Daganzo, C.F. Checkpoint dialaride systems. Transp. Res. Part B Methodol. 1984, 18, 315–327. [Google Scholar] [CrossRef]
 Jaw, J.J.; Odoni, A.R.; Psaraftis, H.N.; Wilson, N.H. A heuristic algorithm for the multivehicle advance request dialaride problem with time windows. Transp. Res. Part B Methodol. 1986, 20, 243–257. [Google Scholar] [CrossRef]
 Cordeau, J.F.; Laporte, G. A tabu search heuristic for the static multivehicle dialaride problem. Transp. Res. Part B Methodol. 2003, 37, 579–594. [Google Scholar] [CrossRef] [Green Version]
 Diana, M.; Dessouky, M.M. A new regret insertion heuristic for solving largescale dialaride problems with time windows. Transp. Res. Part B Methodol. 2004, 38, 539–557. [Google Scholar] [CrossRef] [Green Version]
 Qiu, F.; Li, W.; Zhang, J. A dynamic station strategy to improve the performance of flexroute transit services. Transp. Res. Part C Emerg. Technol. 2014, 48, 229–240. [Google Scholar] [CrossRef]
 Zheng, Y.; Li, W.; Qiu, F. A methodology for choosing between route deviation and point deviation policies for flexible transit services. J. Adv. Transp. 2018, 2018, 6292410. [Google Scholar] [CrossRef] [Green Version]
 Zheng, Y.; Li, W.; Qiu, F. A slack arrival strategy to promote flexroute transit services. Transp. Res. Part C Emerg. Technol. 2018, 92, 442–455. [Google Scholar] [CrossRef]
 Zhang, J.; Li, W.Q.; Wang, G.Q.; Yu, J.C. Feasibility study of transferring shared bicycle users with commuting demand to flexroute transit. Sustainability 2021, 13, 6067. [Google Scholar] [CrossRef]
 Liu, X.; Qu, X.; Ma, X. Improving flexroute transit services with modular autonomous vehicles. Transp. Res. Part E Logist. Transp. Rev. 2021, 149, 102331. [Google Scholar] [CrossRef]
 Zheng, Y.; Gao, L.; Li, W. Vehicle routing and scheduling of flexroute transit under a dynamic operating environment. Discret. Dyn. Nat. Soc. 2021, 2021, 6669567. [Google Scholar] [CrossRef]
 Sun, X.; Liu, S. Research on route deviation transit operation scheduling—A case study in suburb No. 5 Road of Harbin. Sustainability 2022, 14, 633. [Google Scholar] [CrossRef]
 Qiu, F.; Li, W.; Haghani, A. An exploration of the demand limit for flexroute as feeder transit services: A case study in Salt Lake City. Public Transp. 2015, 7, 259–276. [Google Scholar] [CrossRef]
 Han, S.; Fu, H. Optimization of realtime responsive customized bus dispatch. J. Highway Transp. Res. Dev. 2020, 37, 120–127. [Google Scholar]
 Yang, H.; Cherry, C.R.; Zaretzki, R.; Ryerson, M.S.; Liu, X.; Fu, Z. A GISbased method to identify costeffective routes for rural deviated fixed route transit. J. Adv. Transp. 2016, 50, 1770–1784. [Google Scholar] [CrossRef]
 Otto, B.; Boysen, N. A dynamic programming based heuristic for locating stops in public transportation networks. Comput. Ind. Eng. 2014, 78, 163–174. [Google Scholar] [CrossRef]
 Huang, Z.; Liu, X. A hierarchical approach to optimizing bus stop distribution in large and fast developing cities. ISPRS Int. J. GeoInf. 2014, 3, 554–564. [Google Scholar] [CrossRef] [Green Version]
 Ceder, A.; Butcher, M.; Wang, L. Optimization of bus stop placement for routes on uneven topography. Transp. Res. Part B Methodol. 2015, 74, 40–61. [Google Scholar] [CrossRef]
 Johar, A.; Jain, S.S.; Garg, P.K. A conceptual approach for optimising bus stop spacing. J. Inst. Eng. India Ser. A 2017, 98, 15–23. [Google Scholar] [CrossRef]
 Cheng, G.; Zhao, S.; Zhang, T. A bilevel programming model for optimal bus stop spacing of a bus rapid transit system. Mathematics 2019, 7, 625. [Google Scholar] [CrossRef] [Green Version]
 Lyu, Y.; Chow, C.; Lee, V.; Ng, J.K.; Li, Y.; Zeng, J. CBPlanner: A bus line planning framework for customized bus systems. Transp. Res. Part C Emerg. Technol. 2019, 101, 233–253. [Google Scholar] [CrossRef]
 Liu, K.; Liu, J.; Zhang, J. Heuristic approach for the multiobjective optimization of the customized bus scheduling problem. IET Intell. Transp. Syst. 2022, 16, 277–291. [Google Scholar] [CrossRef]
 Ma, C.; Wang, C.; Xu, X. A multiobjective robust optimization model for customized bus routes. IEEE Trans. Intell. Transp. Syst. 2021, 22, 2359–2370. [Google Scholar] [CrossRef]
 Melis, L.; Sörensen, K. The realtime ondemand bus routing problem: The cost of dynamic requests. Comput. Oper. Res. 2022, 147, 105941. [Google Scholar] [CrossRef]
 Karypis, G.; Han, E.H.; Kumar, V. Chameleon: Hierarchical clustering using dynamic modeling. Computer 1999, 32, 68–75. [Google Scholar] [CrossRef] [Green Version]
 Yao, S.; Gu, M. An influence networkbased consensus model for largescale group decision making with linguistic information. Int. J. Comput. Intell. Syst. 2022, 15, 3. [Google Scholar] [CrossRef]
 Bell, J.E.; McMullen, P.R. Ant colony optimization techniques for the vehicle routing problem. Adv. Eng. Inform. 2004, 18, 41–48. [Google Scholar] [CrossRef]
 Yuan, X.; Zhang, Q.; Zeng, J. Modeling and Solution of Vehicle Routing Problem with Grey Time Windows and Multiobjective Constraints. J. Adv. Transp. 2021, 2021, 6665539. [Google Scholar] [CrossRef]
Abbreviations  

Flexroute Transit  FT 
Traditional Transit  TT 
MultiFeaturebased Classification Method  MFCM 
Hierarchical Clustering Algorithm  HCA 
TwoStage Planning Method  TSPM 
MultiRoute Design Model  MRDM 
RouteDesignoriented Genetic Algorithm  RDGA 
Route Modification Model  RMM 
Notations  Description 

$j$  Trip indices 
$k$  Stationvisited order indices 
$l$  Station indices 
$n$  Demand indices 
${N}_{trip}$  The number of trips 
${N}_{j,order}$  $\mathrm{The}\mathrm{number}\mathrm{of}\mathrm{station}\mathrm{visited}\mathrm{orders}\mathrm{of}\mathrm{the}\mathrm{trip}j$ 
${N}_{depot}$  The number of depots 
${N}_{station}$  $\mathrm{The}\mathrm{sum}\mathrm{of}\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{static}\mathrm{stations}\mathrm{and}\mathrm{dynamic}\mathrm{stations},{N}_{station}={N}_{station}^{static}+{N}_{station}^{dynamic}$ 
${N}_{demand}$  $\mathrm{The}\mathrm{sum}\mathrm{of}\mathrm{the}\mathrm{number}\mathrm{of}\mathrm{reserved}\mathrm{demands}\mathrm{and}\mathrm{real}\mathrm{time}\mathrm{demands},{N}_{demand}={N}_{demand}^{reserved}+{N}_{demand}^{realtime}$ 
${S}_{pattern}$  The set of regular travel patterns 
$R{T}_{\left(l,{l}^{\prime}\right)}$  $\mathrm{The}\mathrm{driving}\mathrm{time}\mathrm{from}\mathrm{station}l$$\mathrm{to}{l}^{\prime}$ (min) 
$O{P}_{\left(l,{l}^{\prime}\right)}$  $\mathrm{Binary}\mathrm{value}.1,\mathrm{stations}l,{l}^{\prime}$ are adjacent and situated in opposite position; 0, otherwise 
${O}_{n,l}$  $\mathrm{Binary}\mathrm{value}.1,\mathrm{the}\mathrm{origin}\mathrm{of}\mathrm{a}\mathrm{demand}n$$\mathrm{is}\mathrm{station}l$; 0, otherwise 
${D}_{n,l}$  $\mathrm{Binary}\mathrm{value}.1,\mathrm{the}\mathrm{destination}\mathrm{of}\mathrm{a}\mathrm{demand}n$$\mathrm{is}\mathrm{station}l$; 0, otherwise 
${T}_{n}$  $\mathrm{The}\mathrm{expected}\mathrm{time}\mathrm{of}\mathrm{a}\mathrm{demand}n$ 
${\phi}_{1}$  Unit time cost of a bus (USD/min) 
${\phi}_{2}$  Unit time loss of a passenger (USD/min) 
${\phi}_{3}$  Unit rejection loss of a reserved demand (USD) 
${\phi}_{4}$  Unit rejection loss of a realtime demand (USD) 
$\alpha $  Maximum idle time at a station (min) 
$\beta $  Minimum singletrip time (min) 
$\gamma $  Maximum singletrip time (min) 
$\u03f5$  Maximum waiting time of a passenger (min) 
${x}_{j,k,l}$  $\mathrm{Binary}\mathrm{variable}.1,\mathrm{the}\mathrm{No}.k$$\mathrm{visited}\mathrm{station}\mathrm{of}\mathrm{trip}j$$\mathrm{is}\mathrm{station}l$; 0, otherwise 
${y}_{j,k}$  $\mathrm{Non}\mathrm{negative}\mathrm{variable},\mathrm{representing}\mathrm{the}\mathrm{arrival}\mathrm{time}\mathrm{of}\mathrm{trip}j$$\mathrm{at}\mathrm{No}.k$ visited station 
${z}_{j,n}$  $\mathrm{Binary}\mathrm{variable}.1,\mathrm{the}\mathrm{trip}j$$\mathrm{accepts}\mathrm{the}\mathrm{demand}n$; 0, otherwise 
${p}_{j,\left(l,{l}^{\prime}\right)}$  $\mathrm{Variable},\mathrm{representing}\mathrm{the}\mathrm{OD}\mathrm{pair}\left(l,{l}^{\prime}\right)$$\mathrm{visited}\mathrm{by}\mathrm{trip}j$$,{p}_{j,\left(l,{l}^{\prime}\right)}={{\displaystyle \sum}}_{k=1}^{{N}_{j,order}1}{x}_{j,k,l}{{\displaystyle \sum}}_{{k}^{\prime}=k+1}^{{N}_{j,order}}{x}_{j,{k}^{\prime},{l}^{\prime}}$ 
${t}_{j,\left(k,k+1\right)}$  $\mathrm{Variable},\mathrm{representing}\mathrm{the}\mathrm{arrival}\mathrm{time}\mathrm{difference}\mathrm{of}\mathrm{trip}j$$\mathrm{between}\mathrm{No}.k$$\mathrm{and}\mathrm{No}.k+1$$\mathrm{visited}\mathrm{stations},{t}_{j,\left(k,k+1\right)}={y}_{j,k+1}{y}_{j,k}$ 
${t}_{j,n}$  $\mathrm{Variable},\mathrm{representing}\mathrm{the}\mathrm{time}\mathrm{of}\mathrm{demand}n$$\mathrm{waiting}\mathrm{for}\mathrm{trip}j$$,{t}_{j,n}={{\displaystyle \sum}}_{l=1}^{{N}_{station}}{O}_{n,l}{{\displaystyle \sum}}_{k=1}^{{N}_{j,order}}{x}_{j,k,l}\left({y}_{j,k}{T}_{n}\right)$ 
Parameters  Values  Parameters  Values 

${\phi}_{1}$  USD 1.00/min  $\u03f5$  10 min 
${\phi}_{2}$  USD 0.25/min  ${\rho}_{1}$  0.60 
${\phi}_{3}$  USD 2.50  ${\rho}_{2}$  0.40 
${\phi}_{4}$  USD 5.00  ${N}_{POPUL}$  50 
$\alpha $  1 min  ${N}_{ITER}$  200 
$\beta $  30 min  ${\Delta}_{ITER}$  0.01 
$\gamma $  50 min 
Indices  Description 

OD pairs match rate  The ratio of matched OD pairs to currentdemandrelated OD pairs 
Demands acceptance rate  The ratio of accepted demands to current demands 
Stations effectiveutilization rate  The ratio of accepteddemandrelated stations to all visited stations 
Average passengers per kilometer  The ratio of accepted demands to the total running distance of trips 
Average passengers per trip  The ratio of accepted demands to the number of trips 
Total cost  The sum of bus running cost, passenger waiting time loss, and rejection loss 
Average cost per passenger  The ratio of total cost to accepted demands 
Average waiting time per passenger  The average waiting time of accepted demands 
Average onbus time per passenger  The average onbus time of accepted demands 
Rate difference  $\mathrm{Calculated}\mathrm{by}rat{e}_{ours}rat{e}_{others}$ 
Value difference  $\mathrm{Calculated}\mathrm{by}\frac{valu{e}_{ours}valu{e}_{others}}{valu{e}_{others}}\times 100\%$ 
Time Period  Cluster 1 (Static Stations)  Cluster 2 (Dynamic Stations)  

P ^{1}  L ^{2}  R ^{3}  P ^{1}  L ^{2}  R ^{3}  
07:00:00–08:59:59 (Morning peak)  26  1  19  5  0  8 
09:00:00–15:59:59 (Noon)  21  0  20  5  0  10 
16:00:00–18:59:59 (Evening peak)  33  1  21  3  0  10 
19:00:00–20:59:59 (Night)  26  0  20  4  0  9 
Indices  TT  FT  Differences 

Stations effectiveutilization rate  67.14%  100.00%  32.86% 
Demands acceptance rate  100.00%  91.05%  −8.95% 
Average passengers per kilometer  0.44 P/km ^{1}  0.80 P/km  82.03% 
Average passengers per trip  7.63 P/trip ^{2}  14.46 P/trip  89.53% 
Total cost  USD 31,315.71  USD 19,150.68  −38.85% 
Average cost per passenger  USD 6.11/P ^{3}  USD 4.09/P  −33.08% 
Period  SingleFeature Classification Method  MFCM 

07:00:00–08:59:59 (Morning peak)  4.21  5.95 
09:00:00–15:59:59 (Noon)  3.87  6.76 
16:00:00–18:59:59 (Evening peak)  3.90  7.00 
19:00:00–20:59:59 (Night)  4.09  7.75 
Indices  ThreeStep Method  TSPM  Differences 

OD pairs match rate  90.40%  96.59%  6.19% 
Demands acceptance rate  85.43%  91.05%  5.62% 
Average waiting time of reserved demands  4.84 min  4.07 min  −15.85% 
Average onbus time of reserved demands  10.31 min  9.08 min  −11.95% 
Average waiting time of realtime demands  5.35 min  3.97 min  −25.71% 
Average onbus time of realtime demands  9.46 min  7.89 min  −16.63% 
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, J.; He, Z.; Zhong, J. The MultiType Demands Oriented Framework for FlexRoute Transit Design. Sustainability 2022, 14, 9727. https://doi.org/10.3390/su14159727
Li J, He Z, Zhong J. The MultiType Demands Oriented Framework for FlexRoute Transit Design. Sustainability. 2022; 14(15):9727. https://doi.org/10.3390/su14159727
Chicago/Turabian StyleLi, Jiayi, Zhaocheng He, and Jiaming Zhong. 2022. "The MultiType Demands Oriented Framework for FlexRoute Transit Design" Sustainability 14, no. 15: 9727. https://doi.org/10.3390/su14159727